Subject description

The project aims to better understand how conformational changes are encoded in protein sequences, and to develop new methodology to predict conformational diversity and changes using machine learning. With the help of deep-learning approaches methods to predict flexibility, conformational changes, and structural ensembles will be developed. The project may also involve application of the methodology in the computational design of proteins with the ability to sample conformational states. The methodology can involve the utilization of generative models to sample protein structures, extension of deep-learning frameworks for protein structure prediction, language models and algorithms for morphing and clustering. The recruited candidate will be enrolled in a graduate school in machine learning through WASP.

Wallenberg AI, Autonomous Systems and Software Program (WASP) is Sweden’s largest individual research program ever, a major national initiative for strategically motivated basic research, education and faculty recruitment. The program addresses research on artificial intelligence and autonomous systems acting in collaboration with humans, adapting to their environment through sensors, information and knowledge, and forming intelligent systems-of-systems.

The vision of WASP is excellent research and competence in artificial intelligence, autonomous systems and software for the benefit of Swedish society and industry.

Read more: https://wasp-sweden.org/.

The graduate school within WASP is dedicated to provide the skills needed to analyze, develop, and contribute to the interdisciplinary area of artificial intelligence, autonomous systems and software. Through an ambitious program with research visits, partner universities, and visiting lecturers, the graduate school actively supports forming a strong multi-disciplinary and international professional network between PhD-students, researchers and industry.

Work duties

The doctoral student will mainly work with development of machine learning models for prediction of conformational changes in proteins, with focus on deep learning. The methodolgy may be applied to the design of proteins, for example design of proteins with conformational changes.

The main duties of doctoral students are to devote themselves to their research studies which includes participating in research projects and third cycle courses. The work duties will also include teaching and other departmental duties (no more than 20%).

Admission requirementsA person meets the general admission requirements for third-cycle courses and study programmes if the applicant:

• has been awarded a second-cycle qualification, or
• has satisfied the requirements for courses comprising at least 240 credits of which at least 60 credits were awarded in the second cycle, or
• has acquired substantially equivalent knowledge in some other way in Sweden or abroad.

A person meets the specific admission requirements for third cycle studies in molecular biophysics if the applicant has:

• A minimum of 120 credits are to derive from chemistry courses, including basic courses in biochemistry or cell biology
• At least 30 credits from a second-cycle degree project in the chosen specialisation or a closely related specialisation.
• In certain cases, the requirement for chemistry courses may be replaced by other subjects. The specific entry requirement may also have been obtained through other equivalent education, which is assessed in each individual case.

Additional requirements:

• Very good oral and written proficiency in English.
• Master’s level studies in a program with good preparation for research and appliction of machine learning to molecules, preferably proteins. This can be a program in machine learning, computational biology, computational chemistry, bioinformatics, molecular physics, molecular biotechnology, computer science, with sufficent preparation to complete the WASP graduate school.
• A study background including courses devoted to molecular subjects.
• Demonstrated ability to write scientific code using programming languages such as python and/or C++.

Assessment criteria

Selection for third-cycle studies is based on the student’s potential to profit from such studies. The assessment of potential is made primarily on the basis of academic results from the first and second cycle. Special attention is paid to the following:

Knowledge and skills relevant to the thesis project and the subject of study. An assessment of ability to work independently and to formulate and tackle research problems. Written and oral communication skills Other experience relevant to the third-cycle studies, e.g. professional experience.

Other assessment criteria:

Experience of developing deep-learning methods or more sophisticated machine learning approaches An interest in working in an multidisciplinary research environment and collaborating with other members of the research group doing experimental work or focusing on other areas of computational modeling of proteins. Prior experience of working with proteins is deemed beneficial, but not required.

Consideration will also be given to good collaborative skills, drive and independence, and how the applicant, through his or her experience and skills, is deemed to have the abilities necessary for successfully completing the third cycle programme.

Terms of employment

Only those admitted to third cycle studies may be appointed to a doctoral studentship. Doctoral studentships are regulated in the Higher Education Ordinance (1993:100), chapter 5, 1-7 §§.

The start date is by agreement, but no later than 241215. 

Instructions on how to apply

Applications shall be written in English and include a cover letter stating the reasons why you are interested in the position and in what way the research project corresponds to your interests and educational background. The application must also contain a CV, degree certificate or equivalent, and other documents you wish to be considered (grade transcripts, contact information for your references, letters of recommendation, etc.).