Jobs Posted on the Whova Community Board of GECCO 2022 - The Genetic and Evolutionary Computation Conference
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Fully-funded PhD project with industry partner BAE Systems on “Mitigation of Reinforcement Learning Algorithms in Changing Environments”
The University of Manchester Theme of the PhD project:
The development of (deep) Reinforcement Learning (RL) algorithms to train agents within game environments is well known. Agent training is typically conducted against a known, simplified, or constrained environment. However, the deployed environment is typically more complex, and subject to some change and uncertainly not represented in the training environment. RL algorithms typically characterise performance against probabilistic arenas, rather than being able to cope with an environment that is subject to change over time. The performance of the resulting RL agent can then be expected to become compromised over time, but not necessarily be catastrophic. In this PhD project, we are concerned with (i) understanding this performance degradation and (ii) the development of mitigating strategies. More specifically, the project will focus at creating a train-and-test framework comprising a simulation engine for dynamic environment and a configurable RL approach. In addition to considering changes in the environment, the simulator and RL agent will need to account for real-world challenges, such as multiple conflicting objectives, robustness, and safety issues.
The team at BAE Systems is focused on cutting-edge research in advanced simulation, optimization, and machine learning, and are thus invested in how RL can be extended to support decision making in dynamic environments. The project will therefore contribute directly to BAE Systems’ ongoing research. From a scientific perspective, this project will lead to cross-disciplinary research and output that is of high quality and significance.
Due to the nature of this topic, candidates may be subject to a security check.
2 PhD and 1 Postdoc positions on Distributed Intelligence and Learning
University of Trento For an upcoming European project (starting in October 2022), I am looking for 2 PhD candidates with a Master degree in Computer Science, Information Engineering, or equivalent topics, and 1 postdoc with previous experience in machine learning/evolutionary computation, and, possibly, distributed systems, in particular sensor networks. All the positions are fully funded for 3 years. The PhD candidates must start on November 1, 2022, while the starting date for the postdoc can be negotiated. Each PhD scholarship is approximately 1200 EUR/month (after tax), while the salary for the postdoc is approximately 2500 EUR/month (before tax).
The project will focus on the design and development of lightweight machine learning models for sensor systems to be used in smart building applications.
On the one hand, we will investigate lightweight black-box (e.g., neural networks) and white-box (e.g., decision trees) models, and combinations thereof. In this regard, we will use Neural Architecture Search and evolutionary algorithms to derive optimally designed models that take into account possible computational and energy constraints on the nodes of the distributed system. We will also study the explainability of those models.
On the other hand, we will study different distributed learning approaches, based on consensus and other forms of aggregation of the outputs of the node-local models, in order to achieve a global goal at network level. Federated and split learning approaches, also including probabilistic learning and evolutionary optimization, will be considered to ensure a flexible architecture capable of guaranteeing a proper separation of concerns and data protection/privacy.
For all the positions, previous experience in sensor networks, embedded systems, and probabilistic communication is considered a plus.
AI for enhancing fit and development of body worn products
The University of Manchester About the Project Many of us are familiar with the variation of the fit of high street clothing, however, we rarely see the uniformity of shape expectations embedded in sizing practices in among this variation. Population data is increasingly easy to collect, using image capture technology/body scanning. Whilst our ability to collect population data increases, how we seek to accommodate populations with clothing offerings is extremely limited. The focus of this project is to explore the use of statistical analysis and artificial intelligence (AI) to (i) gain a better understanding of the relationship between the sizing of high street clothing available to the population and what the population actually needs captured in the form of 3-D Body Scans and (ii) support decision making of high street retailers when it comes to clothing sizing offerings.
The starting point of the research are 3-D Body Scans (more than 1000 scans are available) acquired over the past six years at UoM and a database containing commercially available clothing sizes of many high street retailers. Analyzing information extracted from body scans and available sizing offerings can help, for example, identify gaps in the market, explore alternative models of sizing, and study the evolution of the market and population needs and/or over/undersaturation of market segments (using for example clustering analysis). In turn, this information can be used to improve decision making of high street retailers, e.g. the use of (multi-objective) optimization can help retailers in determining which sizes to offer to meet business goals while being inclusive, and novel visualisation tools may find application in presenting better clothing and sizing information to our populations and retailers.
There is sufficient freedom in this project for the candidate to explore additional datasets, propose novel research questions, and develop and apply novel AI methods to improve garment fit and sizing communication.
Graduate Research Assistant or Postdoctoral Research Assistant/Fellow — Developing an Optimisation Platform for type 1 Diabetes Screening
University of Exeter This full-time post is available, on a fixed term basis, from 1st September 2022 to 30th August 2024, subject to the annual funding reactivation applied to awards funded by the JDRF. This role offers the opportunity for hybrid working – some time on campus and some from home.
We are seeking to appoint an enthusiastic Research Associate/Postdoctoral Research Assistant or Fellow to participate in the project “Clinical and Economic Optimisation Platform for Type 1 Diabetes Screening” which is funded by the JDRF, a global organisation funding type 1 diabetes (T1D) research. It is a collaborative project led by the University of Colorado, and which includes a team at the University of Exeter.
The project’s overall aim is to develop a comprehensive, user-friendly, and publicly available clinical and economic T1D screening platform to inform global public health adoption and therapeutic investment decisions. Identifying people at risk of developing type 1 diabetes can help avoid life-threatening complications. Without early identification the consequences are severe, including considerable cost and health consequences for patients and families.
Applicants will be able to develop and calibrate/optimise prediction models, engineer software tools and collaborate effectively with an interdisciplinary and international team of researchers.
The University of Exeter
We are a member of the prestigious Russell Group of research-intensive universities and in the top 150 universities in the world (Times Higher Education World University Rankings 2022 and QS World University Ranking 2022). We combine world-class teaching with world-class research, achieving a Gold rating in the Teaching Excellence Framework Award 2017.
With over 27,000 students and 6,400 staff from 180 different countries we offer a diverse and engaging environment where our diversity is celebrated and valued as a major strength.
3 year postdoc and PhD scholarship on co-evolution
University of Birmingham As part of a UKRI funded project on theory of co-evolutionary algorithms, we are recruiting one postdoc and one PhD student to develop theory of co-evolution. We are particularly interested in candidates with a background in runtime analysis of evolutionary algorithms, algorithmic game theory, or more broadly mathematical approaches in AI. For the PhD candidate, it is additionally beneficial with strong programming skills, eg in OCaml, Python, and/or c with cuda.
Research Associate (Postdoctoral Researcher) in Evolutionary Computation/Design Optimization
The University of New South Wales As a key member of the Multidisciplinary Design Optimization (MDO) research team (http://www.mdolab.net/), the RA will work on an ARC Discovery funded Project. The Research Associate will conduct fundamental research into modelling, solving, benchmarking and analyzing the problems involving hierarchical and conflicting performance objectives. Check the link below for details and feel free to reach out to me for any queries.
Note that due to current uncertainties around visa processing times and travel restrictions, the position requires the candidate to have working rights in Australia.
Postdoc in combinatorial optimisation and continual learning
Edinburgh Napier University This is 28 month position to work as a postdoc on an EPSRC funded project “Keep Learning” that aims to develop an optimisation system for solving combinatorial and constrained problems. The goal is to develop a system that "keeps- learning" in response to a continual instance-stream, rapidly producing optimised solutions to instances and situations that go beyond those envisaged at initial design.
The novelty of the project is in integrating approaches from meta-heuristic search and constrained optimisation with machine-learning techniques (e.g. in continual learning and multi-domain learning) for instance-prediction and algorithm-selection. The post is part of a joint research project in collaboration with the University of St Andrews.
The role holder will conduct research under the supervision of Prof. Emma Hart and in collaboration with the postdoc based in St Andrews to design, implement and evaluate novel machine-learning and evolutionary mechanisms that for example (1) predict future characteristics of instances based on past history (2) generate new instances based on predicted features (3) apply meta-heuristic methods to generate new solvers (4) enable algorithm-selection (5) enable continual adaptation and improvement of the system based on past experiences.
See the link below for a full description
Fully-funded PhD position
Edinburgh Napier University Fully funded PhD position (UK students) to work with Prof. Emma Hart in the Nature Inspired Intelligent Systems group at Edinburgh University. Two topics are available:
1) Evolutionary Robotics Possible avenues of research include the co-evolution of morphology and control of robots the interaction of evolution and learning mechanisms to produce bodies and behaviours that are specialised to specific environments and tasks. Alternative projects might focus on adaptation of behaviour only, using learning methods (e.g. evolution, reinforcement learning) to adapt controllers in real time to adapt to new environments, or learning repertoires of behaviours to enable robust performance. Projects can involve both simulation and hardware experiments.
2) Continual Learning in Combinatorial Optimisation Topics will focus on aspects of algorithm-selection in response to a continuous stream of instances such that an optimiser continuously improves as it exposed to more and more instances. Projects can include aspects of algorithm-generation, automated tuning, and are likely to combine work in machine-learning (continual/transfer learning) with evolutionary optimisation
Technologist/research assistant @ university of Parma, Italy
University of Parma, Italy 22 month appointment starting about November 2022. Applications opening soon (see http://www.unipr.it) Duties : support a project aimed at setting up a data analysis service infrastructure for data coming from spectrometers, oriented to food quality analysis. From a development viewpoint, develop tools aimed at providing easy access to the analysis results. From a research viewpoint, develop machine learning and possibly evolutionary computing algorithms to analyze data.
Competitive salary: about 1700 euros/month net (13 monthly payments) + retirement contributions
Requirements: master’s degree in computer science or computer engineering, PhD in related topics a plus. Skills: programming experience in Python, C++, mobile app development environments.
Term Faculty in Computer Science
American University We're hiring at least one (possibly two) term faculty members in Computer Science. Primary responsibility would be to teach intro classes in Python to CS and Data Science students, with possibly other intermediate or advanced courses as suits a candidate's interest and expertise. Teaching load is 3 classes a semester (20-25 students each; may be possible to arrange as multiple sections of the same course). Requires an MS or PhD in computer science. Full details at the link; I'm also happy to chat about the position.
Research Scientist in the Science team
InstaDeep We are looking for a research scientist to support our new US activities. The science team research lies at the intersection of deep learning, deep reinforcement learning, evolutionary computing and bio-informatics.
In this role at InstaDeep you will report to the Research Lead. You will contribute to knowledge in different fields, and plan and conduct experiments to help expand the canon of the company's scientific knowledge. Among your key responsibilities, we will expect you:
- Collaborate with other researchers and experts to develop machine learning methods for problems in computational biology
- Adapt neural network and evolutionary algorithms to deploy on supercomputing architectures to best exploit modern parallel environments
- Contribute to the team’s publication efforts
- Bridge the gap between the research and product teams by integrating new fundamental research into applied projects
PhD Research Position (evtl. PostDoc) at TU Dresden
TU Dresden Our group (Chair of Big Data Analytics in Transportation) is a young, dynamic, and fast-growing team at the TU Dresden in Dresden, Germany. We are looking for new bright and motivated minds to join our team! If you haven't heard of Dresden before: it is a very liveable, beautiful, and student-friendly city halfway between Berlin and Prague. If you (1) have a background in topics such as Data Analytics, Machine Learning, Optimization, or Benchmarking, (2) have profound programming skills in R or python, and (3) are interested in this vacancy, don't hesitate to contact me for further details!
AI and Evolutionary Researcher (R&D Team)
CloudWalk CloudWalk is an AI first company building its own technology to bring justice to the broken payment system in Brazil. We are building what one would call a "self-driving bank".
The R&D team at CloudWalk does not set clear goals or objectives to its participants. We allow them to explore and propose solutions to any interesting problem that can improve the life of our customers.
We are deeply inspired by the book "Why Greatness Cannot Be Planned: The Myth of the Objective" by Kenneth O. Stanley and Joel Lehman and, for this reason, we consider ourselves as a "non-objective" team, iterating fast and failing fast.
Here are few of our ongoing projects: - Fraud Detection for credit card transaction - Credit Scoring - Auto ML (feature creation, feature selections, hyper parameter optimization, architecture search etc.) - NLP for customer support classification (Portuguese required)
We are looking for people who are critical thinkers, problem solvers, not coders.
More information: https://www.linkedin.com/jobs/view/3152716814/?refId=N%2FTK2p8mczVpC6th8cQX%2Fw%3D%3D&trackingId=JuidG%2FecpD1LMBdsu5UOqA%3D%3D
Lecturer (Assistant Professor) in Computing Science
University of Stirling The Division of Computing Science and Mathematics at the University of Stirling is seeking to appoint a teaching and research lecturer as part of an exciting expansion of our division.
Your research should lie in one of the fields of Artificial Intelligence, including computer vision, machine learning, natural language processing, data science, or human/AI interaction.
EC research would fit in to this as well!
Closing date is 19 July 2022.
More details on the link below.
(I'm posting on behalf of the department but not involved in the appointments process - I'm happy to answer informal questions or connect you with our head of department as needed)
2 x Dual-Award Fully-funded PhDs on Optimization / AutoML / Automatic Algorithm Configuration at University of Manchester and University of Melbourne
University of Manchester, University of Melbourne Application Deadline: 13 July 2022
The University of Manchester and the University of Melbourne are offering two fully-funded scholarships to their dual-award PhD programme that provides a unique experience for PhD candidates wishing to include study abroad as part of their research.
The candidates will be enrolled in the PhD program at the Alliance Manchester Business School at the University of Manchester and in the PhD program at the School of Mathematics and Statistics at the University of Melbourne. Receiving a dual-award allows you to benefit from two world-leading institutions.
1. Manchester-based project: Multi-criteria Automatic Algorithm Configuration under Streaming Problem Instances.
This project aims to extend the capabilities of automatic configuration tools to handle multiple conflicting criteria and adapt to such changes in the problem characteristics. For this purpose, the teams at Manchester and Melbourne will join their expertise in automatic configuration of algorithms and instance space analysis. The result of this project will be more powerful tools for tuning and deploying the critical algorithms that our modern world relies on so that they can better adapt to changes in the problems being solved and let users decide the most appropriate trade-off among conflicting criteria.
Lead supervisor: Manuel López-Ibáñez
2. Melbourne-based project: Explainable Algorithm Selection and Configuration through Instance Space.
This project will develop new analysis methods to help explain the performance (or lack thereof) of the algorithms that are used daily to plan our deliveries, organise our manufacturing plants, schedule our bus routes, optimise our supply chains, etc.
Lead supervisor: Kate Smith-Miles
## Supervision team (of both projects) ##
* Associate Professor Manuel López-Ibáñez, Professor Julia Handl (University of Manchester) * Professor Kate Smith-Miles, Dr Mario Andrés Muñoz-Acosta (University of Melbourne)
https://www.findaphd.com/phds/project/multi-criteria-automatic-algorithm-configuration-under-streaming-problem-instances-manchester-melbourne-dual-award/?p145301 and https://research.unimelb.edu.au/research-at-melbourne/melbourne-and-manchester-graduate-research-group/joint-phd-opportunities/explainable-algorithm-selection-and-configuration-through-instance-space
Researcher in Machine Learning, Optimization and Simulation Models
ArcelorMittal ArcelorMittal Global R&D Asturias Center is looking for a researcher with academic background in Mathematics, Statistics, Physics, Computer Science or equivalent to support ArcelorMIttal Business teams and Industrial Operations (Planning & Scheduling, Logistics, Supply Chain in general, Procurement, Commercial, Corporate and other transversal teams) by developing advanced Artificial Intelligence based models for strategical, tactical and operational decisions. Working with us you will combine your modelling skills, business thinking, mathematical, optimization and simulation methods to build complex algorithms for a wide scope in the Supply Chain, Commercial, Purchasing, Strategy, Finance, Operations and any other area where our models can contribute and facilitate the decision making as part of the evolution towards a complete digitalization.
Researcher in Machine Learning applied to Process Modelling
ArcelorMittal ArcelorMittal Global R&D Asturias is looking for a person that gives support to the Data Modelling Area of the DTS (Digital Transformation Solutions) group. This area develops solutions based on Machine Learning (forecasting, regression, clustering, manifold learning, unsupervised / semi supervised learning, probabilistic models...) and Deep Learning (semantic segmentation, image classification, object detection, generative networks). All these solutions are currently being implemented Worldwide in ArcelorMittal driving the Digital Transformation of the group.
Researcher in Machine Learning/Deep Learning applied to Predictive Maintenance
ArcelorMittal ArcelorMittal Global R&D Asturias is looking for someone with knowledge of Machine Learning/Deep Learning for predictive maintenance. Currently the main research lines go through developing intelligent solutions based on analysis of vibrations, currents, voltages or temperatures. Thus, the development of algorithms using Machine Learning techniques is mandatory. Also, basic knowledge of electrical machines, motors, pumps, drives, etc...and/or demonstrable experience in maintenance works in industrial facilities would be desirable. Finally, knowledge and experience in signal processing also would be desirable.
Postdoctoral Research Fellowship in Interpretable and Fair Machine Learning for Clinical Decision Support
Boston Children's Hospital and Harvard Medical School The Cava Lab at Boston Children’s Hospital / Harvard Medical School is seeking a post-doctoral research fellow to advance the interpretability and fairness of machine learning (ML) models deployed in critical healthcare settings. The fellow will join a multi-disciplinary team of computer scientists, informaticists, clinicians, engineers and bioethicists to develop and assess clinical prediction algorithms and advance our understanding of the behavior of machine learning models deployed in health settings. The fellow will help us think critically about how machine learning methods affect clinical practice and outcomes; in particular, 1) the conditions under which ML provides or fails to provide insight into disease pathologies, and 2) the conditions under which ML exacerbates or mitigates treatment and outcome disparities between patient subgroups.
The fellowship includes an academic appointment at Harvard Medical School, as well as a hospital appointment at Boston Children’s Hospital. This position provides an excellent opportunity for the Research Fellow to work within a multidisciplinary research team to explore advanced areas in health information technology. CHIP is home to 20 faculty working at the forefront of research areas extending beyond clinical prediction algorithms to domains like clinical NLP, digital epidemiology, clinical genomics, and app ecosystems for health records. CHIP and the CAVA Lab value diversity and believe that it is essential to our research goals. We therefore strongly encourage candidates from underrepresented groups to apply. Admissions
The position is available immediately and is renewable annually.
Interested candidates should email a CV, three letters of reference, and a sample publication to Dr. William La Cava, PI Clarity and Virtue-guided Algorithms Lab: email@example.com.
Postdoc in Quantum Optimisation
University of Exeter We have one year postdoc position available on the project "Exploiting Quantum Computing for Large-Scale Transport Models" funded by UKRI starting January 2023. This is a joint project between the University of Exeter (UK) and City Science (https://www.cityscience.com/). The focus will be on modelling transportation problems to run on quantum computers (both on quantum annealers and quantum gate computers). Experience in quantum optimisation desirable. Supervisors: Dr Alberto Moraglio and Prof Ed Keedwell. Please get in touch (soon) if interested, or have questions.
PhD research position
University of Applied Science Upper Austria Integration of Human Expertise and a-priori Knowledge in White-box Machine Learning Employment: 30 hours/week for up to 4 years
This PhD project builds upon and extends the interpretation of learning in AI. Interpretability and explainability are essential prerequisites for involving humans holistically in machine learning processes. For structured numerical data, genetic programming-based symbolic regression is a promising approach to learn complex nonlinear systems behavior in interpretable form. Our main goals will be the integration of knowledge from the domain (e.g. physics, chemistry, mechatronics, automotive, or macroeconomics) like (1) knowledge about correlations between features, (2) knowledge about extrapolation behavior, (3) knowledge about partial model structures (model of models) already during the learning phase.
PostDoc Position at Victoria University of Wellington, New Zealand
Victoria University of Wellington We have one-year postdoc position available on the project " Evolutionary Approaches to Cost-aware Data-Intensive Software Application Multicloud Deployment (MCD)". We will investigate optimisation problems for multicloud deployment and develop novel EC algorithms for finding the best combination of resources. We expect the project results will significantly improve the performance of MCD methods and enhance multicloud resource allocation approaches for data-intensive software.. Supervisors: Associate Professor Hui Ma (https://homepages.ecs.vuw.ac.nz/Users/HMa/WebHome) and Dr. Aaron Chen at ECRG group at Victoria University of Wellington, New Zealand. Please contact us via email: firstname.lastname@example.org, should you have questions or be interested in the position.
PhD position in Machine learning for engineering
IDLab, Ghent university - imec https://sumo.intec.ugent.be/sites/sumo/files//Vacancy_PhD_Student_SUMO_Group_DEML.pdf
The activities of the Ph.D. position are embedded in this stimulating environment with a focus on data-efficient machine
learning (or surrogate modeling) techniques to solve complex and challenging engineering problems with use cases from
various engineering disciplines such as electrical and mechanical engineering.
In particular, the goal of the Ph.D. research is to design tools and techniques to improve key parts of the engineering
design pipeline, aiding designers in several computer-aided design (CAD; design and analysis of computer experiments)
activities such as uncertainty quantification, calibration, sensitivity analysis, design space exploration, and optimization,
etc. Machine learning algorithms such as Gaussian Processes and Bayesian optimization will be used to create a modern
design flow, e.g., for the efficient design of electromagnetic and electronics circuits. This includes techniques for physics-
informed modeling, generative design, preference-based learning, explainable engineering design, spatiotemporal
modeling, free-form topology and shape optimization, efficient data collection, labeling, etc. The proposed Ph.D. research
is defined within the context of several national and international research projects on automation in machine learning