PhD fellowship in Trustworthy Machine Learning
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PhD fellowship in Trustworthy Machine Learning , Department of Computer Science. Faculty of SCIENCE, University of Copenhagen
The Department of Computer Science (DIKU) at the University of Copenhagen invites applicants for a PhD fellowship in Machine Learning with a particular focus on Trustworthy Machine Learning or learning with imperfect data. Start date is (expected to be) on 1 November 1 2024, or as soon as possible thereafter.
The project
As machine learning algorithms become increasingly prevalent in everyday life, needing substantial volumes of user data, it is critical to consider their societal impacts. This project aims to enhance the trustworthiness—encompassing privacy, fairness, and robustness—of contemporary machine learning algorithms, particularly when faced with inadequate data (e.g., noisy, unlabelled, biased, etc.). Consequently, this project will offer an opportunity to delve into a wide array of topics within modern machine learning, including differential privacy, semi- and self-supervised learning, robust machine learning techniques, and ensuring fairness within machine learning models.
Who are we looking for?
We are seeking exceptional candidates with expertise in Machine Learning and Statistics. Ideal applicants may come from a diverse range of areas within machine learning, such as learning theory, robust and private machine learning, as well as semi-supervised or self-supervised learning approaches. This project encompasses both theoretical and applied sides, inviting applicants with a keen interest in either domain to apply. Additionally, individuals with a background in theoretical aspects of Computer Science or Mathematics, who have prior experience with machine learning, are encouraged to join our team.
Our group and research- and what do we offer?
Our group works on various fundamental topics within machine learning with a recent focus on two areas – 1) robust and private machine learning algorithms and 2) machine learning algorithms with limited or imperfect data. We are also a part of the DeLTA Lab, Pioneer Center of AI, and will work closely with BARC. The group is located in the Department of Computer Science (DIKU), Faculty of SCIENCE, University of Copenhagen. We offer a creative and stimulating working conditions in a dynamic and international research environment.
The project will be supervised by Professor Amir Yehudayoff ([email protected]) and Assistant Professor Amartya Sanyal ([email protected]) in the University of Copenhagen and in collaboration with Professor Varun Kanade ([email protected]) at the University of Oxford.
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, Maths, or 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.
Responsibilities and tasks in the PhD programme
We are looking for the following qualifications:
***************************************************************************
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 13 May 2024, 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, several 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 determine 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 http://employment.ku.dk/faculty/recruitment-process/.
Interviews with selected candidates are expected to be held in mid to end June.
Questions
For specific information about the PhD fellowship, please contact Amartya Sanyal, [email protected].
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.
Kilde: Jobnet.dk
The Department of Computer Science (DIKU) at the University of Copenhagen invites applicants for a PhD fellowship in Machine Learning with a particular focus on Trustworthy Machine Learning or learning with imperfect data. Start date is (expected to be) on 1 November 1 2024, or as soon as possible thereafter.
The project
As machine learning algorithms become increasingly prevalent in everyday life, needing substantial volumes of user data, it is critical to consider their societal impacts. This project aims to enhance the trustworthiness—encompassing privacy, fairness, and robustness—of contemporary machine learning algorithms, particularly when faced with inadequate data (e.g., noisy, unlabelled, biased, etc.). Consequently, this project will offer an opportunity to delve into a wide array of topics within modern machine learning, including differential privacy, semi- and self-supervised learning, robust machine learning techniques, and ensuring fairness within machine learning models.
Who are we looking for?
We are seeking exceptional candidates with expertise in Machine Learning and Statistics. Ideal applicants may come from a diverse range of areas within machine learning, such as learning theory, robust and private machine learning, as well as semi-supervised or self-supervised learning approaches. This project encompasses both theoretical and applied sides, inviting applicants with a keen interest in either domain to apply. Additionally, individuals with a background in theoretical aspects of Computer Science or Mathematics, who have prior experience with machine learning, are encouraged to join our team.
Our group and research- and what do we offer?
Our group works on various fundamental topics within machine learning with a recent focus on two areas – 1) robust and private machine learning algorithms and 2) machine learning algorithms with limited or imperfect data. We are also a part of the DeLTA Lab, Pioneer Center of AI, and will work closely with BARC. The group is located in the Department of Computer Science (DIKU), Faculty of SCIENCE, University of Copenhagen. We offer a creative and stimulating working conditions in a dynamic and international research environment.
The project will be supervised by Professor Amir Yehudayoff ([email protected]) and Assistant Professor Amartya Sanyal ([email protected]) in the University of Copenhagen and in collaboration with Professor Varun Kanade ([email protected]) at the University of Oxford.
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, Maths, or 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.
Responsibilities and tasks in the 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 based on your project
We are looking for the following qualifications:
- Professional qualifications relevant to the PhD project
- Relevant publications
- Relevant work experience
- Other relevant professional activities
- Curious mind-set with a strong interest in theoretical and applied aspects of Machine Learning.
- Good language skills
***************************************************************************
Application and Assessment Procedure
Your application including all attachments must be in English and submitted electronically by clicking APPLY NOW below.
Please include:
- Motivated letter of application (max. one page)
- Your motivation for applying for the PhD position including your past experience, why you are a good fit for this PhD position, and what topics you would like to work on.
- Curriculum vitae including information about your education, experience, language skills 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)
- Reference letters (if available)
Application deadline:
The deadline for applications is 13 May 2024, 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, several 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 determine 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 http://employment.ku.dk/faculty/recruitment-process/.
Interviews with selected candidates are expected to be held in mid to end June.
Questions
For specific information about the PhD fellowship, please contact Amartya Sanyal, [email protected].
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.
Kilde: Jobnet.dk
Information og data
Denne ledige stilling har jobtypen "Øvrige", og befinder sig i kategorien "Øvrige stillinger".
Arbejdsstedet er beliggende i København Ø.
Jobbet er oprettet på vores service den 22.3.2024, men kan have været deaktiveret og genaktiveret igen.
Dagligt opdateret: Dette job opdateres dagligt ud fra jobudbyderens hjemmeside via vores søgemaskineteknologi og er aktivt lige nu.
- Øvrige
- København Ø
- Mandag den 13. maj 2024
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