PhD position in efficient deep learning for weakly labelled data
Faculty of Science – Informatics Institute
- Publication date
- 3 September 2018
- Level of education
- Master's degree
- Salary indication
- €2,266 to €2,897 gross per month
- Closing date
- 1 October 2018
- 38 hours per week
- Vacancy number
The Informatics Institute (IvI) of the Faculty of Science invites applications for a PhD candidate in Efficient Deep Learning for weakly labelled data and High-Tech Systems, for a period of four years. The position is part of the Dutch STW Efficient Deep Learning program. You will be based in the Amsterdam Machine Learning Lab (AMLab) led by Prof. Max Welling within the Informatics Institute. The research will be supervised by Prof. Max Welling, and Dr Rianne van den Berg. You will also cooperate with Prof. H. Corporaal in the Electronic Systems group at Eindhoven University of Technology (TU/E). Furthermore, you will spend part of your time at Qualcomm Research Netherlands.
The Efficient Deep Learning (EDL) program
The EDL program combines the fields of machine learning and computing. Both disciplines are strongly represented in the Netherlands and are now connected by seven Dutch academic institutes and more than 35 other (industrial) partners in- and outside the Netherlands. The EDL program contains seven 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. The common goal for all seven EDL projects is to significantly improve the applicability of DL among others by creating data efficient training, and improving computational efficiency, both for training and inference.
The PhD position
As PhD candidate you will be part of the EDL project P4 for deep learning for high-tech systems and materials (HTSM). Partners in this project are the Dutch universities TU/e, UvA and VU, as well as Thermo Fischer, Qualcomm, the Netherlands eScience center, ASTRON and SURFsara. For this position, the main collaborating organizations will be the University of Amsterdam, TU/E and Qualcomm.
You will work as part of the Amsterdam Machine Learning Lab on deep learning methods for HTSM problems with large complex data streams. These data streams are often characterized by large volumes of unlabelled data, with only a small portion of labelled/annotated data points, and extremely unbalanced clusters. You will focus on active learning for efficient labelling strategies, as well as semi-supervised learning. Active learning for weakly labelled data can be aided by calibrated uncertainty estimates in model predictions. Improving uncertainty estimates for deep learning models will thus be a large part of the project.
We are looking for candidates whose skill set matches with (a large part of) the following profile:
- a Master's degree in artificial intelligence, computer science, mathematics, statistics, or closely related area;
- affinity with machine learning and deep learning;
- excellent programming skills (in C, C++, Python);
- excellent mathematical skills (especially in probability theory and statistics, calculus, and linear algebra);
- affinity with signal processing;
- experience with High-Performance Computing (parallel programming, networking, GPUs);
- a team player who enjoys a multicultural and interdisciplinary environment in which academic-industrial collaboration is central;
- commitment and a cooperative attitude;
- strong communication, presentation and writing skills and excellent command of English.
You may send informal inquiries by email to:
Preferred starting date: between October 2018 and February 2019.
The appointment will be full-time (38 hours per week) on a temporary basis for a period of 4 years (initial employment is 18 months). Periodic evaluations will be held after 9 and 14 months, and upon positive evaluation, the appointment will be extended to a total of 48 months. The appointment must lead to a dissertation (PhD thesis). An educational plan that includes attendance of courses, summer and/or winter schools, and national and international meetings will be drafted. We also expect you to assist in teaching of undergraduate students.
The salary is in accordance with the university regulations for academic personnel and will range from €2.266 (first year) up to a maximum of €2.897 (last year) before tax per month (scale P). There are also secondary benefits, such as 8% holiday allowance per year and the end of year allowance of 8.3%. The Collective Labour Agreement for Dutch Universities is applicable.
Among other things, we offer:
- competitive pay and good benefits;
- top-50 university worldwide;
- very friendly, interactive and international working environment;
- excellent computing facilities;
- new building located near the city centre (10 minutes by bicycle) of one of Europe's most beautiful and lively cities.
English is the working language within the Informatics Institute. Since Amsterdam is a very international city where almost everybody speaks and understands English, you need not be afraid of the language barrier.
The UvA is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. We value a spirit of enquiry and endurance, provide the space to keep asking questions and cherish a diverse atmosphere of curiosity and creativity.
You may only submit your application by electronic mail using the link below. We will accept applications until 1 October 2018. To process your application immediately, please quote vacancy number 18-527.
The committee does not guarantee that late or incomplete applications will be considered.
Please do not send or cc your application to Rianne van den Berg. We will consider only applications via the online process.
Your application must include:
- a curriculum vitae;
- a motivation letter that explains why you have chosen to apply for this specific position;
- a copy of your Master’s thesis;
- a complete record of your Bachelor's and Master's courses (including grade transcripts and the explanation of the grading system), and
- the names and contact information of two academic references (please do not include any recommendation letters).
All these should be grouped in a single PDF attachment. #LI-DNP
No agencies please