Doctoral student in Machine Learning for Biological Image Analysis - Hiring in process/Finished, not possible to apply

Project description

Third-cycle subject: Computer Science

We are seeking a motivated and ambitious PhD candidate to join our research team and work on cutting-edge Machine and Deep Learning topics. The positions are based at the Division of Computational Science and Technology at KTH, and the successful candidates will also be part of the SciLifeLab community, Sweden's leading research infrastructure in life sciences.

As a PhD student, you will be working on image-based profiling and generative modeling of biological imaging. You will contribute to the development of machine learning frameworks to address key challenges such as biological phenotype classification, feature embeddings, interpretability of visual differences between biological images. This position offers an exciting opportunity to contribute to the research areas of guided data generation for targeted applications and/or self-supervised learning for large-scale data.

If you are passionate about pushing the boundaries of AI in biology and eager to work in a collaborative, interdisciplinary environment, we invite you to apply and join us in tackling these exciting challenges.

Supervision: Associate Prof. Kevin Smith and Assistant Prof. Gisele Miranda

What we offer

• The possibility to study in a dynamic and international research environment in collaboration with industries and prominent universities from all over the world .
• A workplace with many employee benefits and monthly salary according to KTH's Doctoral student salary agreement.
• A postgraduate education at an institution that is active and supportive in matters pertaining to working conditions, gender equality and diversity as well as study environment.
• Work and study in Stockholm, close to nature and the water.
• Help to relocate and be settled in Sweden and at KTH .
• Opportunity to attend at least one international research conference per year.
• Expert education in a field in great demand nationally and internationally.

Admission requirements

To be admitted to postgraduate education (Chapter 7, § 39 Swedish Higher Education Ordinance), the applicant must have basic eligibility in accordance with either of the following:

• passed a second cycle degree (for example a master's degree), or
• completed course requirements of at least 240 higher education credits, of which at least 60 second-cycle higher education credits, or
• acquired, in some other way within or outside the country, substantially equivalent knowledge

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

Selection

In order to succeed as a doctoral student at KTH you need to be goal oriented and persevering in your work. During the selection process, candidates will be assessed upon their ability to:

• independently pursue his or her work
• collaborate with others,
• have a professional approach and
• analysis and work with complex issues.

Candidate should show openness towards interdisciplinary research.

Experience in Machine Learning/Deep Learning: Solid understanding of machine learning and deep learning algorithms, with demonstrated experience in developing and applying such frameworks in real-world problems.

Programming Skills: Proficiency in a programming language, such as Python, and libraries like TensorFlow, PyTorch, or scikit-learn.

Experience in working with biological data, such microscopic images, is considered a strong merit.

After the qualification requirements, great emphasis will be placed on personal skills.

Target degree: Doctoral degree

Information regarding admission and employment

Only those admitted to postgraduate education may be employed as a doctoral student. The total length of employment may not be longer than what corresponds to full-time doctoral education in four years' time. An employed doctoral student can, to a limited extent (maximum 20%), perform certain tasks within their role, eg training and administration. A new position as a doctoral student is for a maximum of one year, and then the employment may be renewed for a maximum of two years at a time. In the case of studies that are to be completed with a licentiate degree, the total period of employment may not be longer than what corresponds to full-time doctoral education for two years.

Union representatives

Contact information for union representatives.

Doctoral section (Students' union on KTH Royal Institute of Technology)

Contact information for doctoral section .

To apply for the position

Apply for the position and admission through KTH's recruitment system. It is the applicant's responsibility to ensure that the application is completed in accordance with the instructions in the advertisement.

Applications must be received at the last closing date at midnight, CET/CEST (Central European Time/Central European Summer Time).

Applications must include the following elements:

• CV including your relevant professional experience and knowledge.
• Copies of diplomas and grades 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 .
• Representative publications or technical reports. For longer documents, please provide a summary (abstract) and a web link to the full text.

Other information

Striving towards gender equality, diversity and equal conditions is both a question of quality for KTH and a given part of our values.

For information about processing of personal data in the recruitment process.

According to The Protective Security Act (2018-585), the candidate must undergo and pass security vetting if the position is placed in a security class. Information regarding whether the position is subject to such a classification will be provided during the recruitment process.

We firmly decline all contact with staffing and recruitment agencies and job ad salespersons.

Disclaimer: In case of discrepancy between the Swedish original and the English translation of the job announcement, the Swedish version takes precedence.