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Project Description

Doctoral Field: Computer Science

The Division of Robotics, Perception and Learning (RPL) is advertising a doctoral position for a candidate with a background in and interest in computer vision and deep generative learning. The position is part of a WASP-funded project aimed at developing generative methods for better image representations, including for the discovery of new species and identification of data outside the training distribution. The work focuses particularly on the identification of plankton species, with the possibility of integrating genomic data. The algorithms developed will be application-independent, but special focus will be placed on the identification of plankton species, with the goal of building more robust classification systems and systems for identifying new species.

You will become part of an ongoing collaboration between the research groups of University Lecturer Josephine Sullivan at RPL, Professor Anders Andersson (Environmental Genomics), and will take place in collaboration with research groups at RPL, SciLifeLab, and Bengt Karlson at SMHI. The doctoral student will be part of WASP's doctoral school. For more information about scientific results from this collaboration, see the article “Mapping the indispensable minnows of the sea”.

Supervision: Associate Professor Josephine Sullivan and Professor Anders Andersson are proposed to supervise the doctoral student. Decisions are made upon admission.

Admission Requirements

To be admitted to doctoral studies (Chapter 7, Section 39 of the Higher Education Ordinance), the applicant must have basic eligibility:

• completed an advanced level degree, or
• fulfilled course requirements of at least 240 higher education credits, of which at least 60 higher education credits at advanced level, or
• acquired in other ways, within or outside the country, substantially equivalent knowledge.
• The project requires practical competence in deep learning. Documented experience with deep learning programming libraries, such as TensorFlow, PyTorch, or JAX, is therefore a requirement. Experience with GPU-based experiments and cluster computing (e.g., Docker and Slurm) is meritorious.

In addition to the above, there is also a mandatory requirement for English equivalent to English B/6.

Selection

To succeed in your doctoral studies at KTH, you need to be goal-oriented and persistent in your work. In the selection of applicants, the ability to:

• independently drive their work forward,
• collaborate with others,
• have a professional approach, and
• analyze and work with complex issues.

In the assessment of candidates, special weight will be placed on academic merits, completed courses, and documented programming skills, for example through completed project works. Previous specialization in computer vision and/or machine learning is highly desirable and particularly meritorious.

After meeting the eligibility requirements, great importance will be attached to personal qualities.

Target Degree: Doctorate

Information about Admission and Employment

Only those admitted to doctoral studies may be employed as doctoral students. The total employment period may not be longer than what corresponds to full-time doctoral education over four years. An employed doctoral student may, to a limited extent (maximum 20%), perform certain tasks within, for example, education and administration. A new employment as a doctoral student is valid for a maximum of one year; the employment may thereafter be renewed for a maximum of two years at a time. For studies to be concluded with a Licentiate degree, the total employment period may not be longer than what corresponds to full-time doctoral education over two years.

As a doctoral student, you are entitled to KTH's employment benefits and have a monthly salary according to KTH's agreement for doctoral salaries. Read more about Doctoral Studies at KTH | KTH | Sweden.

Union Representatives

Contact details for union representatives.

Doctoral Student Section (Student Union of the Royal Institute of Technology)

Contact details for the doctoral student section.

Application

You apply via KTH's recruitment system. As an applicant, you are primarily responsible for ensuring your application is complete when submitted.

The application must contain the following:

• Copies of degree certificates and transcripts from previous university studies and certificates of fulfilled language requirements (see above). Translations into English or Swedish if the original document is not issued in one of these languages. Copies of originals must be certified.
• CV with relevant professional experience and skills.
• Representative publications or technical reports. For longer documents, please attach an abstract and a web link to the full text.

The application must be received by KTH no later than the last day of application at midnight, CET/CEST (Central European Time/Central European Summer Time).

Other

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