PhD position on Deep Learning for High Tech Systems and Materials
PhD position on Deep Learning for High Tech Systems and Materials
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Job description
Eindhoven University of Technology (TU/e) has a vacancy for a
PhD position on Deep Learning for High Tech Systems and Materials
(Project P4 within the Dutch Efficient Deep Learning program)
within the Electronic Systems group, department of Electrical Engineering.
Deep Learning in context
Deep learning has dramatically improved the state-of-the-art in object detection, speech recognition, robotics, and many other domains. Whether it is superhuman performance in object recognition or beating human players in Go, the astonishing success of deep learning (DL) is achieved by deep neural networks trained with huge amounts of training examples and massive computing resources. Although already applied successfully in academic use cases and several consumer products (e.g. automatic language translation), its data and computing requirements pose all kind of efficient DL challenges for further market penetration like less time for training, low energy consumption, and the use on small embedded processing platforms as used in many systems.
The Efficient Deep Learning (EDL) program
The EDL program combines the fields of machine learning and computing: both disciplines are already strong in the Netherlands and now connected by 7 Dutch academic institutes and more than 35 other (industrial) partners in- and outside the Netherlands. The EDL program contains 7 use case driven EDL research projects: P1) DL as a service, P2) Reconstruction, matching and recognition, P3) Video analyses and surveillance, P4) High tech systems and materials, P5) Human and animal health, P6) Mobile robotics, and P7) DL platforms. Common goal for all 7 EDL projects is to significantly improve the applicability of DL, among others by creating data efficient training, and tremendously improving computational efficiency, both for training and inference.
The PhD position
Partners in EDL-P4 are the Dutch universities TU/e, UvA and VU, and also Thermo Fischer, Qualcomm (Scyfer), NLeSc, ASTRON and SURFsara. The PhD candidate will work, in the Electronic Systems group www.es.ele.tue.nl/ml-at-es.php at the TU/e, on the development of energy and resource efficient DL algorithms and circuits for high performance embedded systems. This involves system modeling, and development of advanced training algorithms that work well with limited amounts of labeled data and are able to generate confidence bounds for the predictions made. He/she will work on implementations to accelerate DL by programmable hardware (e.g. FPGAs, GPUs).
The specific DL use case concerns the application of DL for high-performance electron microscopy, in close cooperation with Thermo Fischer, https://www.fei.com. Electron microscopy enables e.g. the study of biological tissue properties and cell structures, to obtain in-depth knowledge on disease properties and possible treatments.
He/she will also cooperate with the Amsterdam Machine Learning Lab (AMLAB) of the University of Amsterdam, amlab.science.uva.nl, especially for Methods for semi-supervised learning and active labeling, where the goals are to use both labeled and unlabeled data in training a classifier, to train classifiers when the number of (labeled) examples is small and to detect bad labels and to suggest informative examples to be labeled.</p
PhD position on Deep Learning for High Tech Systems and Materials
(Project P4 within the Dutch Efficient Deep Learning program)
within the Electronic Systems group, department of Electrical Engineering.
Deep Learning in context
Deep learning has dramatically improved the state-of-the-art in object detection, speech recognition, robotics, and many other domains. Whether it is superhuman performance in object recognition or beating human players in Go, the astonishing success of deep learning (DL) is achieved by deep neural networks trained with huge amounts of training examples and massive computing resources. Although already applied successfully in academic use cases and several consumer products (e.g. automatic language translation), its data and computing requirements pose all kind of efficient DL challenges for further market penetration like less time for training, low energy consumption, and the use on small embedded processing platforms as used in many systems.
The Efficient Deep Learning (EDL) program
The EDL program combines the fields of machine learning and computing: both disciplines are already strong in the Netherlands and now connected by 7 Dutch academic institutes and more than 35 other (industrial) partners in- and outside the Netherlands. The EDL program contains 7 use case driven EDL research projects: P1) DL as a service, P2) Reconstruction, matching and recognition, P3) Video analyses and surveillance, P4) High tech systems and materials, P5) Human and animal health, P6) Mobile robotics, and P7) DL platforms. Common goal for all 7 EDL projects is to significantly improve the applicability of DL, among others by creating data efficient training, and tremendously improving computational efficiency, both for training and inference.
The PhD position
Partners in EDL-P4 are the Dutch universities TU/e, UvA and VU, and also Thermo Fischer, Qualcomm (Scyfer), NLeSc, ASTRON and SURFsara. The PhD candidate will work, in the Electronic Systems group www.es.ele.tue.nl/ml-at-es.php at the TU/e, on the development of energy and resource efficient DL algorithms and circuits for high performance embedded systems. This involves system modeling, and development of advanced training algorithms that work well with limited amounts of labeled data and are able to generate confidence bounds for the predictions made. He/she will work on implementations to accelerate DL by programmable hardware (e.g. FPGAs, GPUs).
The specific DL use case concerns the application of DL for high-performance electron microscopy, in close cooperation with Thermo Fischer, https://www.fei.com. Electron microscopy enables e.g. the study of biological tissue properties and cell structures, to obtain in-depth knowledge on disease properties and possible treatments.
He/she will also cooperate with the Amsterdam Machine Learning Lab (AMLAB) of the University of Amsterdam, amlab.science.uva.nl, especially for Methods for semi-supervised learning and active labeling, where the goals are to use both labeled and unlabeled data in training a classifier, to train classifiers when the number of (labeled) examples is small and to detect bad labels and to suggest informative examples to be labeled.</p
Specifications
- max. 38 hours per week
- Eindhoven View on Google Maps
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Eindhoven University of Technology (TU/e)
Requirements
We are looking for candidates who's skills match with (a large part of) the following profile:A master's degree in Computer Science, Electrical Engineering, Artificial Intelligence or related disciplines with excellent grades. Very good programming skills (e.g., in C, C++, Python). Familiarity with deep learning libraries. Some experience with digital hardware design (e.g., VHDL, Verilog or HLS). Affinity with Signal Processing, Machine Learning, and a good mathematical background are important. A team player that enjoys to work in multicultural teams. Good communication and organization skills. Excellent English language skills (writing and presenting). </ul
Conditions of employment
We offer a fixed-term, 4 year position in a research group with an excellent reputation. Salary and benefits are in accordance with the Collective Labor Agreement for Dutch Universities, including:A gross monthly salary between EUR 2222,- (first year) and EUR 2840- (last year). Additionally, 8% holiday and 8.3% end-of-year annual supplements. A minimum of 41 holidays per year (excluding bank holidays, for a full-time employment of 40 hrs/week). Additional benefits, including excellent technical infrastructure, child care, holiday savings schemes, and sports facilities. Assistance for finding accommodation is offered. Personal development program aimed to develop your social and communication skills (see www.tue.nl/PROOF3TU ). </ul
