BEMÆRK: Ansøgningsfristen er overskredet
In this 3-year PhD project within the fields of data science, machine learning, and enzyme engineering you will be part of an extraordinarily ambitious development project with (1) the technological aim of developing Bayesian optimization frameworks for the rapid evolution of enzymes (2) the humanitarian aim of paving the way for a more sustainable future.
The position is a grant between DTU Bioengineering, DTU Biosustain, and DTU Compute and part of the Digitalization and Data Science PhD programme. The position commences in Spring 2024.
The project
The enhancement of enzyme catalytic efficiency in varied contexts is vital for both research and industry. This process is influenced by multiple factors, including the choice of reactants, catalysts, solvents, and conditions like temperature, pressure, and pH balance. Traditional single-variable optimization falls short because these factors are interdependent, making multi-variable optimization complex. To address this, computational methods in machine learning (e.g., Bayesian neural networks) offer robust solutions for optimizing experimental conditions. These methods utilize predictive models to estimate performance metrics and their uncertainties for new conditions. Through a balanced approach that includes testing untested conditions and honing in on the most promising ones, these models support iterative testing and learning. This process allows for a targeted exploration of areas with high potential, leading to more efficient optimization. Despite the potential, the scarcity of applied examples and a solid framework for comparing algorithms remain obstacles to the widespread adoption of these computational methods in enzyme reaction optimization. To bridge this gap, this initiative proposes to create a virtual environment for comparing various algorithms and to develop a specialized multi-objective model for optimizing enzyme performance metrics.
Digitalization and Data Science PhD
This initiative follows a strategy to educate the next generation of bilingual biotechnology scientists at the intersection of data science, engineering, and life sciences. Bilingual scientists capable of translating domain-specific (e.g., process systems engineering and multi-scale modelling, bio- discovery, scalability, and manufacturability) problems into data science and engineering solutions are highly needed. Further, bilingual profiles are the hardest to find and the most demanded in industry and academia. We want to fill this gap and offer multiple data science-focused PhD projects supervised by at least two of the involved departments and by helping candidates to customize their course curricula to ensure that they become bilingual in the domains covered in the project. Your personal efforts will enable and accelerate development of new environmentally friendly products, new medical treatments, climate friendly farming, etc. by applying/developing tools and methods for extracting insights from life sciences data.
DTU Bioengineering: The Digital Biotechnology Lab (Asst. Prof and Head of Data Science Timothy Jenkins)
Part of the Technical University of Denmark and DTU Bioengineering, the DBL is a research group within the Center for Antibody Technologies. We are passionate about harnessing the power of modern technology to innovate the biotechnological landscape and develop real world solutions for the benefit of society. Primarily, we focus on leveraging the power ML to help identify novel protein targets and discover therapeutic, diagnostic, and reagent binding proteins. Specifically, we are interested in (1) de novo
peptide sequencing, and (2) ML-guided binder discovery approaches, such as generative protein design. Importantly, this not only allows us to pursue fundamental questions, such as unravelling the core rule set that dictates binder-target interactions, but also translational endeavours, such as discovering cost-effective therapeutics against infectious and neglected tropical diseases amongst others. The research group is headed by Assistant Professor Timothy Patrick Jenkins. We are a group of high performing younger scientists that share the vision that science is supposed to benefit humankind, especially underprivileged societies and/or societal groups. This shared vision with a very international outreach has created an academic environment that is ambitious and innovative, fuelled by a collegially supportive attitude to both people and their work. We welcome people with all types of backgrounds and strive for diversity. Our research group is highly active in numerous outreach activities and our international travel agendas are typically busy. Finally, group members typically have an entrepreneurial attitude, resulting in the establishment of a number of spin-out biotech projects.
See more: www.digital-biotechnology.com
DTU Biosustain: Enzyme Engineering and Structural Biology (Senior Researcher Ditte Welner)
The Enzyme Engineering and Structural Biology (EE&SB) group develops and utilizes enzymology to tackle the biodiversity crisis. Using structural biology and protein chemistry as our primary tools, we strive to take a modern approach to these well-established disciplines, with multidimensional experimental setup and increased throughput. Our primary techniques are X-ray crystallography, rational design, machine learning, and HPLC. We are part of the Novo Nordisk Center for Biosustainability. Here, we build our science on the observation that recent progress in our ability to read and write genomic code, combined with advances in automation, analytics and data science, has fundamentally changed the scope and ambition of harnessing the potential of biological systems. Big data approaches and analysis of biological systems are key research instruments at the Center. DTU Biosustain utilizes these advances for biosolutions design to foster sustainable lifestyles in relation to three application areas: Sustainable Chemicals, Natural Products, and Microbial Foods
DTU Compute: Statistics and Data Analysis (Professor Line Clemmensen)
The Section of Statistics and Data Analysis at DTU Compute conducts research and machine learning and statistical methodology with applications in life sciences. We provide a strong foundation to select, develop, and evaluate models like generative autoencoders, gaussian processes, Bayesian optimization, and machine learning in general. You will become part of a research group headed by Professor Line Clemmensen, where we strive to develop models with excellent performance, in particular under distribution shifts and low resource conditions.
Research and Development Opportunities
You will get the opportunity to learn and apply the latest algorithms/data science methods related to synthetic biotechnology and biosustainability. Further, during the selection process, you will define together with your supervisors (main and co-supervisors) the most relevant courses from the domains covered in the project (30 ECTS points of relevant PhD courses as required in Danish PhD programs). These courses will ensure that you become a bilingual scientist with deep knowledge in data science as well as biology, chemistry, computation, etc. We anticipate that by working with us, you will foster some of the following skills:
- Opportunity to work across a multi-disciplinary research program
- Access to world leading companies and initiatives within industrial biotechnology
- Transferable skills across bioengineering, process systems engineering and computer science
- Opportunity to tap into an agile innovation ecosystem
- Bioprocess development across scales in a new data-driven paradigm
- Development and application of state-of-the-art machine learning and deep learning on biotechnological problems
Responsibilities
As a PhD student embarking on a three-year journey in biotech-focused data science and machine learning, you will spearhead a pioneering project aimed at advancing enzyme optimization through Bayesian frameworks. This role not only demands scientific acumen but also leadership, project management, mentoring, and the ability to communicate complex research to the global scientific community. Here's a snapshot of the key responsibilities you will hold:
- Scientific Leadership: Lead research in Bayesian optimization, fostering interdisciplinary collaboration and innovation in enzyme engineering.
- Research Project Management: Strategically drive the project from conception through to publication, ensuring adherence to scientific and funding objectives.
- Student Supervision: Guide and support students' research, promoting their academic growth and contributions to the project's success.
- Scientific Dissemination: Actively publish and present research findings, enhancing the project's visibility and impact in the scientific community.
Requirements
Two-year master's degree (120 ECTS points) or equivalent in computational biology, bioinformatics, machine learning, or related topics.
Ideal competencies required for the projects
You are willing to work in an international environment with colleagues and partners from all over the world. The following competencies are advantageous for successful execution of this PhD project:
- MSc in data science, machine learning, computer science, biology, bioengineering or related fields
- Strong background in data science, machine learning, deep learning, and/or bioinformatics including programming and mathematical modelling
- Good understanding of biology and enzymes
- Proficiency in software development and data management (Python, R, and other relevant programming language)
- Interest in sustainability and biotechnology challenges
- Ability to translate domain-specific problems into data science and engineering solutions
- Excellent communication and interpersonal skills for working in a multi-disciplinary team environment
- Knowledge of multi-scale modeling and in-silico experimentation
- Knowledge of process modeling and simulation
If you have these competencies and experience listed above, and you have initiative, are motivated, and eager to learn more and further improve, then you are good to go.
You can look forward to being part of a leading research and education institution in Europe where on-going development of skills and knowledge is a part of the foundation. You will have great flexibility, as trust and respect are some of the values we build our results on.
Approval and Enrolment
The scholarship for the PhD degree is subject to academic approval, and the candidate will be enrolled in one of the general degree programs at DTU. For information about our enrolment requirements and the general planning of the PhD study program, please see DTU's rules for the PhD education
.
We offer
DTU is a leading technical university globally recognized for the excellence of its research, education, innovation and scientific advice. We offer a rewarding and challenging job in an international environment. We strive for academic excellence in an environment characterized by collegial respect and academic freedom tempered by responsibility.
Salary and terms of employment
The appointment will be based on the collective agreement with the Danish Confederation of Professional Associations. The allowance will be agreed upon with the relevant union. The period of employment is 3 years.
You can read more about career paths at DTU here http://www.dtu.dk/english/about/job-and-career/working-at-dtu/career-paths.
The positions will be located at the DTU Lyngby campus in a newly established research building hosting ultra-modern laboratories for antibody discovery and phage display technology with neighbouring laboratories with state-of-the-art facilities for protein science and proteomics.
Further Information
Additional information may be obtained from Asst. Prof. Timothy Patrick Jenkins, tpaje@dtu.dk
You can read more about the department at www.bioengineering.dtu.dk/
If you are applying from abroad, you may find useful information on working in Denmark and at DTU at DTU – Moving to Denmark . Furthermore, you have the option of joining our monthly free seminar “PhD relocation to Denmark and startup “Zoom” seminar ” for all questions regarding the practical matters of moving to Denmark and working as a PhD at DTU.
Application procedure
Please submit your online application no later than 15 January 2024 (23:59 Danish time).
Applications must be submitted as one PDF file containing all materials to be given consideration. To apply, please open the link "Apply online," fill in the online application form, and attach all your materials in English in one PDF file . The file must include:
- Application (cover letter)
- CV
- Grade transcripts and BSc/MSc diploma (in English) including official description of grading scale
- List of publications
- Names and contact details of 2 academic referees
You may apply prior to obtaining your master's degree but cannot begin before having received it.
Applications received after the deadline will not be considered.
All interested candidates irrespective of age, gender, race, disability, religion or ethnic background are encouraged to apply.
INFORMATIONER OM STILLINGEN:
- Arbejdspladsen ligger i:
Lyngby-Taarbæk Kommune
-Virksomheden tilbyder:
-Arbejdsgiver:
Danmarks Tekniske Universitet, Anker Engelunds Vej, 2800 Kongens Lyngby
-Ansøgning:
Ansøgningsfrist: 15-01-2024; - ansøgningsfristen er overskredet
Se mere her: https://job.jobnet.dk/CV/FindWork/Details/5958552
