Three PhD fellowships in Machine Learning

Department of Computer Science
Faculty of SCIENCE
University of Copenhagen

Department of Computer Science, Machine Learning Section invites applicants for the PhD fellowships in the following projects.

The project
At least three open positions are being advertised in this call. Each position is detailed below

• Trustworthy Machine Learning- The student will work on a combination of topics in robustness, privacy (specifically Differential Privacy), and unlearning within Machine Learning. The project can focus either on a theoretical perspective on learning theory or on applying these concepts to modern machine learning practices. Applicants must have strong skills in mathematics, especially in probability, statistics, and linear algebra. A solid background in machine learning and experience with programming in a machine learning context are also required. While knowledge of learning theory, optimization theory, strong coding experience, experiments with large machine learning models, and previous publications in the field will be viewed favorably, they are not mandatory. Prospective applicants are encouraged to visit this link for more information.The project will be supervised by Amartya Sanyal and Yevgeny Seldin. For any inquiries, please contact Amartya Sanyal at [email protected].
  
• Robust Online Learning – The project will explore the impact and mitigation of adversarial threats in online learning, specifically in distributed (a.k.a. federated) settings. Growing interest in public-domain utilization of federated learning, such as in healthcare and media recommendation, has led to significant developments in protecting models against adversarial manipulation in the training phase. Prior work, however, largely focuses on supervised learning and ignores the paradigm of online learning. The latter is becoming more relevant in this age of language models, where we seek to adapt models on the fly, using real-time information. To prevent these language models from getting manipulated by poisonous or erroneous information (a.k.a. misinformation), it is of critical importance to develop robust online learning algorithms. To learn more on the need and challenges of robust machine learning (ML), check out the introductory content of this book.Candidates interested in this project are expected to have a strong background in ML and optimization. Knowledge of distributed algorithms and stochastic processes is a plus. Project will be supervised by Nirupam Gupta and Yevgeny Seldin. Contact person: Nirupam Gupta ([email protected]).
  
• Sustainable Machine Learning - In this project, we will broadly investigate resource-efficient machine learning (ML) methods and their effect on the sustainability of ML. This can be at the level of developing novel algorithms, learning paradigms, or hardware optimization techniques that can result in reductions in the resources required when developing and deploying ML pipelines.The interplay of resource efficiency with the broader sustainability of ML (in terms of safety, fairness, and access) will be of particular interest. For more details about the project visit the project page here.The project will be supervised by Raghavendra Selvan and Erik B. Dam. For any further questions, contact Raghavendra Selvan at [email protected].
• Motivated letter of application clearly mentioning the project you are applying for (max. two pages)
• Curriculum vitae including information about your education, experience, relevant courses, language skills, names and email addresses of three referees, and other skills relevant for the position
• Original diplomas for Bachelor of Science or Master of Science and transcript of records in the original language, including an authorized English translation if issued in another language than English or Danish. If not completed, a certified/signed copy of a recent transcript of records or a written statement from the institution or supervisor is accepted.
• Publication list (if possible)

Candidates applying for this position must have strong skills in mathematics and ML, with a drive towards advancing the UN sustainable development goals.

Important: When applying, write down Amartya Sanyal, Nirupam Gupta, or Raghavendra Selvan in the Principal Supervisor field to indicate which project you are applying to.
Our group and research- and what do we offer?
The Machine Learning Section is a part of the Department of Computer Science, Faculty of SCIENCE, University of Copenhagen, and the ELLIS Unit Copenhagen (https://ellis.eu/). The department is heading 2 centers within Artificial Intelligence: the SCIENCE AI Center and the Pioneer Center within Artificial Intelligence. The University of Copenhagen was founded in 1479 and is the oldest and largest university in Denmark. It is ranked as the best university in Scandinavia and as one of the top places in Europe.

The Department of Computer Science offers a friendly and thriving international research and working environment with opportunities to build up internationally competitive research groups. Copenhagen is one of the 10 most livable cities in the world with a rich culture within music, theater and associations. Life for families is made easy by a publicly supported daycare and health care system, dual career opportunities, maternity/parental leave and six weeks of paid annual vacation. International candidates may find information on living and working in Denmark here. Useful information is also available at The International Staff Mobility office (ISM) at the University of Copenhagen (link). ISM offers a variety of services to international researchers coming to and working at the University of Copenhagen.

The PhD programme
Depending on your level of education, you can undertake the PhD programme as either:

Option A: A three year full-time study within the framework of the regular PhD programme (5+3 scheme), if you already have an education equivalent to a relevant Danish master’s degree.

Option B: An up to five year full-time study programme within the framework of the integrated MSc and PhD programme (the 3+5 scheme), if you do not have an education equivalent to a relevant Danish master´s degree – but you have an education equivalent to a Danish bachelors´s degree.

Option A: Getting into a position on the regular PhD programme
Qualifications needed for the regular programme
To be eligible for the regular PhD programme, you must have completed a degree programme, equivalent to a Danish master’s degree (180 ECTS/3 FTE BSc + 120 ECTS/2 FTE MSc) related to the subject area of the project, e.g. Computer Science, Electrical Engineering, Mathematics, Operations Research, and Statistics. For information of eligibility of completed programmes, see General assessments for specific countries and Assessment database.

Terms of employment in the regular programme
Employment as PhD fellow is full time and for maximum 3 years.

Employment is conditional upon your successful enrolment as a PhD student at the PhD School at the Faculty of SCIENCE, University of Copenhagen. This requires submission and acceptance of an application for the specific project formulated by the applicant.

Terms of appointment and payment accord to the agreement between the Danish Ministry of Taxation and The Danish Confederation of Professional Associations on Academics in the State. The position is covered by the Protocol on Job Structure.

Option B: Getting into a position on the integrated MSc and PhD programme
Qualifications needed for the integrated MSc and PhD programme
If you do not have an education equivalent to a relevant Danish master´s degree, you might be qualified for the integrated MSc and PhD programme, if you have an education equivalent to a relevant Danish bachelor´s degree. Here you can find out, if that is relevant for you: General assessments for specific countries and Assessment database.

Terms of the integrated programme
To be eligible for the integrated scholarship, you are (or are eligible to be) enrolled at one of the faculty’s master programmes in Computer Science.

Students on the integrated programme will enroll as PhD students simultaneously with completing their enrollment in this MSc degree programme.

The duration of the integrated programme is up to five years, and depends on the amount of credits that you have passed on your MSc programme. For further information about the study programme, please see: www.science.ku.dk/phd, “Study Structures”.

Until the MSc degree is obtained, (when exactly two years of the full 3+5 programme remains), the grant will be paid partly in the form of 48 state education grant portions (in Danish: “SU-klip”) plus salary for work (teaching, supervision etc.) totalling a workload of 150 working hours per year.
A PhD grant portion is currently (2024) DKK 6,820 before tax.

When you have obtained the MSc degree, you will transfer to the salary-earning part of the scholarship for a period of two years. At that point, the terms of employment and payment will be according to the agreement between the Ministry of Finance and The Danish Confederation of Professional Associations on Academics in the State (AC). The position is covered by the Protocol on Job Structure.

Responsibilities and tasks in both PhD programmes

• Complete and pass the MSc education in accordance with the curriculum of the MSc programme

(ONLY when you are attending the integrated MSc and PhD programme)

• Carry through an independent research project under supervision

• Complete PhD courses corresponding to approx. 30 ECTS / ½ FTE
• Participate in active research environments, including a stay at another research institution, preferably abroad
• Teaching and knowledge dissemination activities
• Write scientific papers aimed at high-impact journals
• Write and defend a PhD thesis on the basis of your project

We are looking for the following qualifications:

• Professional qualifications relevant to the PhD project
• Strong interest in the relevant topics
• Academic background (e.g. courses taken) in the relevant topics
• Good English language skills
• (Optional) Relevant publications
• (Optional) Relevant work experience

Application and Assessment Procedure
Your application including all attachments must be in English and submitted electronically by clicking APPLY NOW below.

Please include:

Application deadline:
The deadline for applications is 15th January, 2025 23:59 CET.
We reserve the right not to consider material received after the deadline, and not to consider applications that do not live up to the abovementioned requirements.

The further process
After deadline, a number of applicants will be selected for academic assessment by an unbiased expert assessor. You are notified, whether you will be passed for assessment.

The assessor will assess the qualifications and experience of the shortlisted applicants with respect to the above mentioned research area, techniques, skills and other requirements. The assessor will conclude whether each applicant is qualified and, if so, for which of the two models. The assessed applicants will have the opportunity to comment on their assessment. You can read about the recruitment process at https://employment.ku.dk/faculty/recruitment-process/.

Interviews with selected candidates are expected to be held in mid-to-end February 2025.

Questions
For specific information about the PhD fellowship, please contact the relevant supervisors who emails are provided above.

General information about PhD study at the Faculty of SCIENCE is available at the PhD School’s website: https://www.science.ku.dk/phd/.

The University of Copenhagen wishes to reflect the surrounding community and invites all regardless of personal background to apply for the position.

Københavns Universitet giver sine knap 10.000 medarbejdere muligheder for at udnytte deres talent fuldt ud i et ambitiøst, uformelt miljø. Vi sikrer traditionsrige og moderne rammer om uddannelser og fri forskning på højt internationalt niveau. Vi søger svar og løsninger på fælles problemer og gør ny viden tilgængelig og nyttig for andre.

Info
Ansøgningsfrist: 15-01-2025

Ansættelsesdato: 01-08-2025

Arbejdstid: Fuldtid

Afdeling/Sted: Datalogisk Institut