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Assistant/Associate Professor of Human-Centric AI
LUCID @ University of Nottingham
We are looking to recruit several Assistant/Associate Professors in Human-Centric AI to the School of Computer Science to complement our strengths and support our Lab for Uncertainty in Data and Decision Making (LUCID) research group.

At LUCID, we are working to develop tomorrow’s approaches to human-centric AI, combining fundamental research with applications ranging from decision support in environmental management to cyber security, and consumer-driven product design. Much of our work seeks to bring together information captured from contributors including experts with traditional quantitative data, in order to help decision-makers make more comprehensively informed—and better decisions.

We are a diverse team in computer science cognisant of the breadth of the cross-disciplinary challenge in delivering human-centric AI. If you share our passion for advancing AI-driven decision support with the view to maximise its benefit to stakeholders by conducting fundamental and applied research and developing tomorrow’s techniques in human-centric AI—get in touch.
AI Specialist
baker hughes
Join our innovating Artificial Intelligence Team

Our TPS team provides industry-leading products and services that optimize the production and processing of energy. We help a diverse range of customers across the value chain to reduce operating costs and improve productivity. Our team looks at the development and creation of the future of energy technology products.

Partner with the best

You will be instrumental in our mission to make energy safer, cleaner and efficient through intelligent technologies. You will implement and adapt AI algorithms to solve complex problems. You will contribute to our future as an AI player in the Energy Transformation industry.

As a Lead AI Specialist, you will be responsible for:

Checking availability and relevance of internal and external data sources; clean and validate data

Proposing and leading new data collection activities, if necessary

Contributing to AI algorithms development: model problems, discover insights, apply AI, mining, statistical and visualization techniques

Deepening your knowledge on a specific AI technology domain (CV, NLP, ...)

Fuel your passion

To be successful in this role you will:

Be a graduate in computer science, mathematics or equivalent. Have minimum 5+ yrs of industry experience.

Have advanced knowledge of ML, at least one domain out of Computer Vision, NLP, Time Series Analysis

Have advanced knowledge of general-purpose programming language (Java, Python, R, ...) and of agile practices and principles

Have basic knowledge of database, large datasets management and big data frameworks (Dask, Spark, Hadoop)

Have excellent motivation skills, with ability to deliver outcomes to strict deadlines

Have capability for problem solving and root cause analysis; ability to rapidly understand and learn new
Full Professor in Artificial Intelligence and Machine Learning
Edinburgh Napier University
Edinburgh Napier University
Salary: £65,573 to £106,482 per annum (Grade 8 – 10)

Edinburgh Napier University is the ‘#1 Modern University in Scotland’. An innovative, learner centric university with a modern and fresh outlook, Edinburgh Napier is ambitious, inclusive in its ethos and applied in its approach.

The professor will be based at the School of Computing, Engineering & the Built Environment located in the lively and exciting Merchiston area at the heart of Edinburgh, Scotland’s inspiring capital.

The latest UK national research assessment, REF 2021, places our Computer Science research in the top-30 in the whole UK and 3rd best in Scotland (both in power ranking). Our research is underpinned by significant amounts of funding from prestigious sources including both EPSRC and Horizon 2020.

We are looking for an experienced academic (currently an Associate or Full Professor or equivalent) with expertise in Artificial Intelligence and Machine Learning or related fields. Areas of desirable expertise include, but are not limited to: machine learning with applications to robotics, machine learning theoretical foundations, machine learning applied to biomedical data and healthcare, search-based optimisation, deep learning systems, adversarial machine learning, generative models in machine learning, natural language processing with machine learning, explainable machine learning, neuromorphic machine learning systems. With 80% time allocation for research, this role will allow you to explore novel and emerging areas of artificial intelligence and machine learning, deliver excellent quality research papers and secure substantial external research funding.

Informal enquiries about the role can be made to Professor Peter Andras ( - attending WCCI / IJCNN 2022 and may be available for in-person meetings.
Application closing date: END OF AUGUST 2022
Interview date: SEPTEMBER 2022
Link: None
Postdoctoral Fellow in Machine Learning and Neuroimaging
The Center for Translational Research in Neuroimaging and Data Science
The Post-Doctoral Associate in this position will work in a multi-disciplinary and multi-institutional collaborative environment developing models capable of learning from high-dimensional brain imaging data and transfer to application settings where labels are rarely available and data is usually scarce. The person in this position will work under the supervision of Dr. Sergey Plis, Associate Professor in Computer Science and Director of the TReNDS Machine Learning Core, creating models that are able to learn from already available data of patients and controls and transfer learned representations to Alzheimer's disease research applications simplifying interpretation of data and biomarker's discovery. The focus is on enabling learning from datasets of small and moderate sizes and producing model introspection methods capable of generating interpretable biomarkers. The person will process data and train deep learning models on a local cluster and in the AWS cloud. Software development is done in python in a specific pytorch ecosystem framework. A good understanding of software development practices is a plus.

Find further details here
NLP researcher
Leonardo S.p.A.
We are searching for an NLP researcher to work in the following topics:
- News retrieval using Deep Learning approaches
- Text analysis to extract meta-data like: aspect based sentiment, topic, NER, etc.
- GPT based (e.g. version 2 or open one) training on custom documents
- Knowledge graphs construction support, using NLP technologies (coreference, NER and linking, relation extractions, etc...)
- Development of distributed training on our multi-GPU HPC davinci-1

Used framework: Pytorch

Hybrid Remote Working
NLP PhD Thesis (CIFRE): Temporality Extraction from French Clinical Text
PhD thesis funded by a French Healthcare startup (Posos) and in partnership with HeKA team (Inria-Inserm) in Paris, under the supervision of Pr. Antoine Neuraz.

The thesis subject is in the NLP domain, applied to clinical text, and focuses on temporality extraction. This task consists in identifying pathologies, drugs, and procedures, and linking them to specific
concepts from a terminology and thus requires an understanding of semantic relations between terms.

Additional details are provided in the linked document, but please note that one requirement is to be able to speak French, at least to some extent, as the thesis focuses on French data.
postdoctoral research in ML and AI
University of South Dakota
postdoctoral research in ML for biomedical engineering data analysis.
PDRA & PhD Positions in AI/Robotics for Material Discovery at the University of Liverpool, UK
University of Liverpool
We have several roles (postdocs/phds) in our lab. Please have a look at the specific adverts:
PostDoc at Victoria University of Wellington, New Zealand
Victoria University of Wellington
We have two-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 ( and Dr. Aaron Chen at ECRG group at Victoria University of Wellington, New Zealand. Please contact us via email:, should you have questions or be interested in the position.
Open careers in is a company sponsore of WCCI 2022 and working in NLP and AI fields.

Please, have a look to open careers:
Computer Vision on Small Annotated Datasets Researcher
Leonardo S.p.a.
The aim of the role is to study and to develop methodologies and architectures for training models with very few annotated data. Common techniques in this field span from synthetic data generation to generative models or Meta Learning and Self-supervise Learning. Within this project we will focus on Meta Learning and Self-supervised Learning.

If you are interested in this role you can apply through this link :
Research Associate in Evolutionary Computation/Design Optimization
The University of New South Wales (UNSW), Canberra, Australia
We have an open position for Research Associate in Evolutionary Computation/Design Optimization in our research group ( Our group is located in the School of Engineering and Information Technology (SEIT), University of New South Wales (UNSW), Canberra, Australia. Please follow the link below for the position description and application. Feel free to reach out to me for any related queries.
WASP fellow professor or associate prof. (tenure)
Luleå University of Technology
For our gender-balanced machine learning group in Sweden, we search and outstanding candidate to join us working on basic research on ML, contributing to the wellfare of the society. it comes with a 2.5 million EUR welcome package for typically 2 PhDs, 2 Postdocs, and special hardware.

benefit from an extraordanary welcoming and open environment in the worlds most innovative country

enjoy the great Swedish way, hospitality, social security, and highest quality of life

collabborate with our ELLIS follows, award winning researchers, and promising students from Sweden and abroad.

interested? text me, if you are outstanding and interested to move to the best place to live (in my opinion - I tried Germany, Switzerland, and Japan, and many other countries for short visits)
Automated Product Design: MSc, PhD, Post-Doctoral & Internships (2022-2025)
University of Cape Town
Several MSc, PhD, post-doctoral and research assistantships (2022-2025) are available at the Evolutionary Machine Learning Group, Department of Computer Science, University of Cape Town. All candidates will be physically based at the Evolutionary Machine Learning Group, UCT, Cape Town, where the research is done in collaboration with colleagues at Smarter Sorting, Austin, Texas, USA. For the purposes of the research assistantships, working online from remote locations will also be considered.
Automated Product Design: Currently consumer products ranging from plastics, to cosmetics to batteries are manufactured from complex compositions of chemical elements and pre-designed chemical substances. An ongoing challenge in the design of new products is to: 1) minimise manufacturing and material costs, 2) minimise the expected environmental impact of the product (during manufacturing and after consumer use), and to 3) only use specific (regulated) materials and chemical compositions. Thus, novel product design can be formulated as a multi-objective optimisation problem. Specifically, where the design of any new product simultaneously satisfies all these constraints, but the product designer is able to manage the weighting (relative importance) of each design objective (constraint). This enables the automated production of a broad array of new products that satisfy the design objectives to varying degrees. A product designer would then ideally select one of several automatically designed products according their own specific constraints for how expensive a product can be, what materials can be used in its manufacture, and what the extent of its expected environmental impact can be. This research investigates multi-objective evolutionary algorithms to automate the design of a vast array of products, given a pre-defined set of materials and chemical substances usable in the design and manufacturing processes, and metrics for expected economic and environmental cost.
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