Job Information
- Organisation/Company
- University of Twente (UT)
- Research Field
- Technology
- Researcher Profile
- First Stage Researcher (R1)
- Country
- Netherlands
- Application Deadline
- Type of Contract
- Temporary
- Job Status
- Not Applicable
- Hours Per Week
- 40.0
- Is the job funded through the EU Research Framework Programme?
- Not funded by an EU programme
- Is the Job related to staff position within a Research Infrastructure?
- No
Offer Description
The vacancy is focused on calibration in deep learning. Deep Neural Networks (DNNs) have demonstrated significant predictive capabilities across various domains including computer vision, speech recognition, and natural language processing. Existing sophisticated neural network architectures are frequently integrated into practical applications. However, recent research has highlighted a crucial issue: despite their high accuracy achieved through training, deep neural networks often have overconfident and underconfident predictions. Deploying uncalibrated models in real-world systems poses substantial risks, particularly in safety-critical contexts like monitoring critical infrastructure systems. Calibrating deep learning models is essential to mitigate this risk, ensuring that the model's posterior distribution accurately represents uncertainty without being excessively overconfident.
The PhD candidate is supposed to carry out research on calibration in deep learning at the Pervasive Systems Research group, Department of Computer Science, Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) at the University of Twente in the Netherlands. The candidate is expected to closely collaborate with the project partners such as Vrije Universiteit, Saxion, TNO-ESI and industrial partners ASML, Canon Production Printers, ITEC, Philips, and ThermoFisher Scientific.
The main research objectives are:
- Conduct research in calibrating AI, including but not limited to designing and implementing calibrated deep neural models, conducting experiments, analyzing data, and interpreting results.
- Collaborate with the team to explain the root causes of model miscalibration, optimize diagnostic processes for monitoring for Cyber-Physical System diagnostics.
- Write technical reports and research papers for publication in top-tier journals and conferences (Percom, Ubicomp, IPSN, SenSys, IJCAI, AAAI, NIPS, ICML).
Requirements
- The ideal candidate has a Master’s degree in either Computer Science, Electronic Engineering, Telecommunication Engineering, or Mathematics
- You have solid background and strong experience in deep learning
- Familiarity with Cyber-Physical Systems and systems diagnostics
- You should be interested in solving analytical tasks, conduct experiments, and develop prototypes, combined with intermediate programming skills (e.g. in R, Python or C-something)
- Prior experience in calibrating AI, generative AI is a plus (with frameworks such as TensorFlow and Pytorch)
- You have a strong personality to defend your research ideas not only at university but also in an industrial context
- You have good communication skills, and you have a strong interest in operating at the crossroads of different disciplines
- You have an excellent command of English (C1; above IELTS 6.5 or equivalent)
- You are able to do independent research and have publication skills
Additional Information
- As a PhD candidate at UT, you will be appointed to a full-time position for four years, with a qualifier in the first year, within a very stimulating and exciting scientific environment;
- The University offers a dynamic ecosystem with enthusiastic colleagues;
- Your salary and associated conditions are in accordance with the collective labour agreement for Dutch universities (CAO-NU);
- You will receive a gross monthly salary ranging from € 2.770,- (first year) to € 3.539,- (fourth year);
- There are excellent benefits including a holiday allowance of 8% of the gross annual salary, an end-of-year bonus of 8.3%, and a solid pension scheme;
- The flexibility to work (partially) from home;
- A minimum of 232 leave hours in case of full-time employment based on a formal workweek of 38 hours. A full-time employment in practice means 40 hours a week, therefore resulting in 96 extra leave hours on an annual basis;
- Free access to sports facilities on campus
- A family-friendly institution that offers parental leave (both paid and unpaid);
- You will have a training programme as part of the Twente Graduate School where you and your supervisors will determine a plan for a suitable education and supervision;
- We encourage a high degree of responsibility and independence, while collaborating with close colleagues, researchers and other staff.
Are you interested in this position? Please send your application via the 'Apply now' button below before 19/05/2024, and include:
- A cover letter (maximum 2 pages A4), emphasizing your specific interest, qualifications, motivations to apply for this position.
- A Curriculum Vitae, including a list of all courses attended and grades obtained, and, if applicable, a list of publications and references.
- An IELTS-test, Internet TOEFL test (TOEFL-iBT), or a Cambridge CAE-C (CPE). Applicants with a non-Dutch qualification and who have not had secondary and tertiary education in English can only be admitted with an IELTS-test showing a total band score of at least 6.5, internet. TOEFL test (TOEFL-iBT) showing a score of at least 90, or a Cambridge CAE-C (CPE).
- A short description (maximum 1 page of A4) of your MSc research
- Names and contact details of 2-3 referees (they will be approached only if the candidate is shortlisted).
For more information regarding this position, you are welcome to contact Duc Le Viet (v.d.le@utwente.nl)
- Website for additional job details
Work Location(s)
- Number of offers available
- 1
- Company/Institute
- Universiteit Twente
- Country
- Netherlands
- City
- Enschede
- Postal Code
- 7522NB
- Street
- Drienerlolaan 5
- Geofield
Where to apply
- Website
Contact
- City
- Enschede
- Website
- Street
- Drienerlolaan 5
- Postal Code
- 7522 NB