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PhD-TA in Robust Machine Learning for Dynamic and Out-of-Distribution Data

• Personeelstype: Wetenschappelijk personeel

• Vakgebied: Promovendus

• Organisatie: Department of Mathematics and Computer Science

• Soliciteer voor: 01-06-2025

• Voltijds equivalent: 1.0 FTE

• Salaris: € 2.901 - € 3.707

Eindhoven University of Technology is an internationally top-ranking university in the Netherlands that combines scientific curiosity with a hands-on attitude. Our spirit of collaboration translates into an open culture and a top-five position in collaborating with advanced industries. Fundamental knowledge enables us to design solutions for the highly complex problems of today and tomorrow.

With over 110 (assistant, associate and full) professors, almost 300 PhD and EngD students, about 1500 Bachelor students and 1000 Master students, the Department of Mathematics and Computer Science (M&CS) is the largest department of the ¹û¶³´«Ã½. By performing top-level fundamental and applied research, and maintaining strong ties with industry, M&CS aims to contribute to science and innovation in and beyond the region.

Introduction

Are you passionate about exploring robust machine learning models for temporal data? Join our research team to explore cutting-edge methods that improve predictive models' robustness to distribution shifts, data noise, and other real-world challenges.

Job Description

Are you excited to tackle challenges in machine learning and data mining? In this PhD position, you will explore cutting-edge machine learning methods aimed at improving the robustness of predictive models, with a particular focus on temporal data—data that captures how systems evolve over time.

Robustness is essential for deploying machine learning models in real-world settings where data distributions can shift unexpectedly due to changes in environment, behavior, or context. Models that lack robustness may perform well in controlled settings but fail in practice, especially when exposed to Out-of-Distribution (OOD) data. Ensuring reliable performance in such scenarios is key for building systems that can be trusted in high-stakes domains like healthcare, social platforms, and e-commerce.

Your research will tackle several key challenges, including but not limited:

• Out-of-Distribution (OOD) Generalization: You will investigate how to design models that generalize well to unseen distributions, such as domain shifts and temporal drifts. This includes studying algorithms that models domain-invariant representation learning, adaptive modeling, or causaliy driven approaches.
• Multi-modal Modeling: Real-world systems often involve multiple data sources (e.g., text, time series, sensor data). You will explore how to understand, integrate and model this multi-modal information to enhance prediction accuracy and model resilience.
• Event Sequence Modeling: Many human-centered applications involve irregular, context-dependent event sequences (e.g., protests, clinical visits) that are sensitive to distribution shifts. You will develop explainable models for predicting human event occurrences and ensuring transferability to unseen social scenarios, studying advances in sequential deep learning and large language models (LLMs).

You will work with experts from machine learning and data mining. You work will contribute to the .

Job Requirements

• A master’s degree (or an equivalent university degree) in Computer Science or a closely related field.
• Solid background in machine learning and mathematics.
• Strong programming skills in Python.
• Enthusiastic and motivated in research.
• Experience in related topics is preferred.
• Good academic writing.
• Interested in collaborating with industrial partners.
• Fluent in spoken and written English (C1 level).

Conditions of Employment

• A meaningful job in a dynamic and ambitious university, in an interdisciplinary setting and within an international network. You will work on a beautiful, green campus within walking distance of the central train station. In addition, we offer you: 
• Full-time employment for five years, with an intermediate assessment after nine months. You will spend  20% of your five-year employment on teaching tasks, with a maximum of 25% per year of your employment.
• Salary and benefits (such as a pension scheme, paid pregnancy and maternity leave, partially paid parental leave) in accordance with the Collective Labour Agreement for Dutch Universities, scale P (min. € 2,901 max. € 3,707).  
• A year-end bonus of 8.3% and annual vacation pay of 8%. 
• High-quality training programs and other support to grow into a self-aware, autonomous scientific researcher. At ¹û¶³´«Ã½ we challenge you to take charge of your own learning process. 
• An excellent technical infrastructure, on-campus children's day care and sports facilities.  
• An allowance for commuting, working from home and internet costs. 
• A Staff Immigration Team and a tax compensation scheme (the 30% facility) for international candidates. 

Information

Do you recognize yourself in this profile and would you like to know more? Please contact the hiring manager Amy Deng, Assistant Professor, Department of Mathematics and Computer Science, s.deng@remove-this.tue.nl or Mykola Pechenizkiy, Professor, Department of Mathematics and Computer Science, m.pechenizkiy@remove-this.tue.nl.

Visit our website for more information about the application process or the conditions of employment. You can also contact HRServices.MCS@remove-this.tue.nl.

Are you inspired and would like to know more about working at ¹û¶³´«Ã½? Please visit our career page.

Application

We invite you to submit a complete application by using the apply button. The application should include a:

• Cover letter in which you describe your motivation and qualifications for the position.
• Curriculum vitae, including a list of your publications and the contact information of three references.

We look forward to receiving your application and will screen it as soon as possible. The vacancy will remain open until the position is filled.

Referentienummer: 2025/158