PhD opening
Telecom Paris
Deep Neural Networks can solve extremely challenging tasks thanks to complex stacks of
(convolutional) layers with thousands of neurons, especially to solve computer vision-related
tasks like image classification, object detection or image segmentation [He et al.][Krizhevsky et
al.][Simonyan and Zisserman]. Their success comes from their ability to learn from examples,
not requiring any specific expertise and using very general learning strategies, based on loss’
minimization.
In the last years a lot of focus has been devoted over two different aspects related to deep
learning and computer vision:
● How can we improve the deep models learning strategy? How can we make these
models focus on extracting the “right” information from the input, in an automatic way?
● Do we really need all the complexity we are currently using to solve tasks with deep
learning? Is there going to be any difference at training and at inference time?
Candidate profile
● Strong competences and knowledge in Machine Learning and Deep Learning.
● Master of Science in Computer Science, Electronic Engineering, Datascience or related
fields.
● Deep knowledge of Deep learning libraries like Tensorflow/PyTorch, advanced coding
skills in Python and C.
● Great passion and strong commitment towards research in AI.
● (optional) Publication in AI conferences/journals.
How to apply?
● Send Curriculum Vitae, transcript of the studies and motivation letter to
enzo.tartaglione@telecom-paris.fr
PostDoc opening
Telecom Paris
For the proposed postdoc opening, the candidate will be in charge of supporting the research activity (including following the work of masters and PhD students) in the field of deep neural network compression, debiasing and privacy preservation.
Programming skills with the most common deep learning libraries (tensorflow/pytorch) mandatory.
Looking for highly motivated profiles, already holding the PhD in computer science or related fields or defending soon.
Starting date: as soon as possible, negotiable.
Length: 1 / 1.5 year
Salary: standard, determined on the school basis.
How to apply?
● Send Curriculum Vitae to
enzo.tartaglione@telecom-paris.fr
Research Engineer Vienna
Cogvis gmbh
At cogvis we combine novel hardware technology and state of the art computer vision to develop products that provide an actual benefit to people. Our main product is cogvisAI, an AI-based system that keeps elderly people safe and supports their caretakers. We are an agile and innovative team and utilize the latest and greatest tech stacks available, such as modern deep learning libraries and algorithms, AWS cloud-native computing, and modern web frameworks.
Join our team as a Research Engineer, be part of a fast-growing startup with an amazing team spirit, and enjoy a flexible career path and various benefits! As part of our research group, you’ll have the opportunity to interact with an international research community, explore new technologies, and shape the future of our products and services.
Tasks
Your main tasks are:
• Development of prototype software for national and international research projects in the fields Computer Vision, Artificial Intelligence, and Internet of Things
• Managing hardware and test setups, data acquisition, and labeling
• Maintenance and extension of software developed as part of previous research projects
You’ll work in a small research team with a team leader that handles project management and external communication.
Requirements
Link:
www.cogvis.ai
COMPUTER VISION AND MACHINE LEARNING EXPERT
University of Verona and EVS Embedded Vision Systems S.r.l.
We are hiring talents with skills and experience in computer vision and machine/deep learning to work on highly innovative projects addressing challenging, realistic, yet still open problems in CV&ML and related applications.
We are looking for passionate researchers who loves challenges and wants to grow working in a team composed by academia and industry scholars, highly qualified and engaged to study, design, develop and validate image and video analysis methods based on deep learning approaches.
Main topics are related to domain adaptation and generalization, semi/self/un-supervised learning, learning with imbalance/few data or with (label) noisy data.
Required qualifications (not all requested, but the more, the better):
· Significant skills and experience in Machine and Deep Learning.
· Significant skills and experience in Computer Vision and Image\Signal Processing.
· Experience in Software development using Python (SciPy, NumPy, OpenCV, Pillow, etc.).
· Experience with development frameworks for machine learning such as TensorFlow/Keras and PyTorch.
· Knowledge of state-of-the-art DNN architectures such as ResNet, MobileNet, YOLO, etc.
· Knowledge about object detection and classification, human pose estimation, semantic segmentation, forecasting.
· Publication track record on relevant conferences (CVPR, ECCV, ICCV, NeurIPS, ICLR, ICML, etc.) and/or journals (PAMI, IJCV, CVIU, etc.)
· Problem-solving, analytical and critical thinking skills.
· Excellent communication skills and ability to work in a highly collaborative environment.
· Fluent English, spoken and written.
Doctorate studies are a very welcome add-on, even if not mandatory.
Place of work: Verona, Italy.
If you are interested and meet the above requirements, please, send your CV and other possible relevant information to Prof. Vittorio Murino, vittorio.murino@univr.it.
I’m at ICIAP, you can also look for me for a chat or more info.
Thank you
Link:
None
PostDoc and PhD openings
PAVIS IIT
The Pattern Analysis and Computer Vision (PAVIS, https://pavis.iit.it/) research line at IIT will soon open multiple Ph.D. and Postdoc positions. PAVIS is looking for highly motivated researchers with a relevant background in Computer Vision and Machine Learning.
The main mission of PAVIS is to design and develop innovative AI solutions mostly related – but not limited– to Computer Vision and 3D reasoning. PAVIS researches and develops computational tools for the large-scale understanding of data in order to provide assistive AI systems to support human beings in their daily life, with a privileged focus on image and video data but also leveraging multi-modal information (e.g. 3D data, audio, text).
Join a group of more than 30 researchers developing novel computer vision models and architectures related to self- and unsupervised learning, graph neural networks, active vision and curiosity-driven learning, dynamic scene graphs for 3D scene understanding, and multi-modal learning. PAVIS research is also supported by IIT Franklin HPC, a 64 node, 256 GPUs computing infrastructure for large scale training and testing of machine learning models.
PAVIS is currently looking for candidates with expertise in computer vision, pattern recognition, computer science or related areas. Evidence of top-quality research on the above specified areas in the form of published papers in top-tier conferences and journals is a plus for selection.
The workplace is Genova, Italy at the Center for Human Technologies (CHT).
Link:
https://pavis.iit.it
Deep Learning Researcher - Trustworthy AI
Leonardo S.p.A.
Trustworthy AI is one of the most interesting research field in Deep Learning. Studying and developing methods and architectures that improve the trustworthy of a Deep Learning model, exploring interpretability, robustness and explainability, is extremely important, in particular in industrial applications. This position is open to a researcher with a minimum of Master Degree in a scientific discipline (e.g. Engineering, Mathematics, Physics, Statistics) and experience in Deep Learning. A phd is a plus. The position is hybrid (remote+presence) located in Rome/Genoa. The candidate will collaborate with other universities (industral phd researchers) focused on this topic.
Link:
https://www.leonardo.com/en/focus-detail/-/detail/a-new-recruitment-drive-to-strengthen-the-innovation-ecosystem-focus
Deep Learning Researcher - Self Supervised Learning
Leonardo S.p.A.
This position is open to a researcher that want to focus on the architectures and approaches to solve the problem of small data annotations. Self-supervised learning is an exciting approach that could be very useful to solve (when it is possible) this problem. The main activities of this position are:
- adapt models and implement new methods of SSL (e.g. extening VISSL torch library) mainly in the computer vision fields (rgb, infrared, hyperspectral domain)
- support european funded projects
- create a company modelZOO of pretrained models
- Explore new paradigm of learning for the cause (e.g. meta-learning)
Link:
https://www.leonardo.com/en/focus-detail/-/detail/a-new-recruitment-drive-to-strengthen-the-innovation-ecosystem-focus
PhD student
University of Udine
Applications are invited for a PhD position at the University of Udine (Italy), to work on traffic monitoring, in cooperation with Comark srl (Udine). Candidates are encouraged to contact me (https://fusiello.github.io) before end of July 2022.
Link:
https://fusiello.github.io
