PhD student in Machine Learning for life science within WASP Graduate SchoolLund University, Faculty of Science, Department of ChemistryLund University was founded in 1666 and is repeatedly ranked among the world’s top 100 universities. The University has around 44 000 students and more than 8 000 staff based in Lund, Helsingborg and Malmö. We are united in our efforts to understand, explain and improve our world and the human condition. Lund University welcomes applicants with diverse backgrounds and experiences. We regard gender equality and diversity as a strength and an asset. Subject description We are looking for a PhD student in Machine Learning applied to life science within WASP, placed at the department of biochemistry and structural biology. Work duties The Wallenberg AI, Autonomous Systems and Software Program (WASP) is a major national initiative for strategically motivated basic research, with the goal of advancing Sweden into an internationally recognized and leading position in the areas of artificial intelligence, autonomous systems and software. The main focus of the research within WASP is artificial intelligence and autonomous systems acting in collaboration with humans, adapting to and learning from their environment through sensors, information and knowledge, forming intelligent systems-of-systems. Read more at https://wasp-sweden.org. The WASP Graduate School is dedicated to provide the skills needed to analyze, develop, and contribute to the interdisciplinary area of artificial intelligence, autonomous systems and software. The curriculum provides the foundations, perspectives, and state-of-the-art knowledge in the different disciplines taught by leading researchers in the field. Read more at https://wasp-sweden.org/graduate-school. The focus of the research is to develop machine learning methods to model and design the three-dimensional structure of proteins. Deep learning approaches (such as AlphaFold) has recently revolutionized protein structure prediction, enabling highly accurate predictions of the atomic structures based on amino acid sequence information. Similar advancements are expected for the inverse problem, finding amino acid sequences that encode a desired atomic structure. This is referred to as protein design and has many applications in biomedicine, biotechnology and material science. The project aims to develop deep generative models to design proteins that can simultaneously adopt two conformations. Machine learning methods will be combined with state-of-the-art approaches for computational protein design. 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 can also include teaching and other departmental duties (no more than 20%). Admission requirements A person meets the general admission requirements for third-cycle courses and study programmes if he or she:
Additional requirements:
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: Strong background in mathematics and computer programming is highly beneficial. Prior background in machine learning is advantageous. Experience working with biological data (protein sequence and structure in particular) is beneficial, but not required. Study background in chemistry and biology 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 §§. 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.). Eligibility Students with basic eligibility for third-cycle studies are those who- have completed a second-cycle degree- have completed courses of at least 240 credits, of which at least 60 credits are from second-cycle courses, or- have acquired largely equivalent knowledge in some other way, in Sweden or abroad. The employment of doctoral students is regulated in the Swedish Code of Statues 1998: 80. Only those who are or have been admitted to PhD-studies may be appointed to doctoral studentships. When an appointment to a doctoral studentship is made, the ability of the student to benefit from PhD-studies shall primarily be taken into account. In addition to devoting themselves to their studies, those appointed to doctoral studentships may be required to work with educational tasks, research and administration, in accordance with specific regulations in the ordinance. Type of employment Limit of tenure, four years according to HF 5 kap 7§. The Faculty of Science conducts research and education within Biology, Astronomy, Physics, Geosciences, Chemistry, Mathematics and Environmental Science. The Faculty is organized into nine departments, gathered in the northern campus area. The Faculty has approximately 1500 students, 330 PhD students and 700 employees. We kindly decline all sales and marketing contacts. Type of employment Temporary position longer than 6 months
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