The biotech industry is constantly searching for the perfect mutation, where properties from different proteins are synthetically combined to achieve a desired effect. It may be necessary to develop new medicaments or enzymes that prolong the shelf-life of yogurt, break down plastics in the wild, or make washing powder effective at low water temperature. New research from DTU Compute and the Department of Computer Science at the University of Copenhagen (DIKU) can in the long term help the industry to accelerate the process. In the journal Nature Communications, the researchers explain how a new way of using Machine Learning (ML) draws a map of proteins, which makes it possible to appoint a candidate list of the proteins that you need to examine more closely. In recent years, we have started to use Machine Learning to form a picture of permitted mutations in proteins.

Candidates should have a two-year master's degree (120 ECTS points) or a similar degree with an academic level equivalent to a two-year master's degree. We expect that you have studied topics in computer science, mathematics, or similar including experience in image analysis, computer vision, machine learning, etc. Furthermore, the ability to program in Python, Matlab, C, or similar is important. Also, the ability to work in a multidisciplinary environment is essential, as is a good command of the English language. Multispectral imaging can be used for indirectly measuring the microorganisms associated with the plant leaves.

Right from old-fashioned matchmaking to modern dating services, romantic matchmakers have focused on what singles themselves desired when they assisted them in the hunt for their soulmate. In other words, there has been nothing decisively new under the sun for several hundred years. At the request of DR3, researchers at DTU Compute have developed a self-learning algorithm and sent it in search of the recipe for a good relationship. "The algorithm receives a huge amount of information about each individual person in each of the 667 relationships, for example about food and transport habits, childhood town, height, number of brothers and sisters, pets, consumption patterns, and much more--including things that are not normally regarded as relevant to our choice of partner. The algorithm then looks for a pattern in the relationship based on the information. In this way, the algorithm itself learns what the ingredients in a stable relationship are, and how they are to be mixed," explains the creator of the algorithm, Professor Jan Larsen from DTU Compute.

You will be part of the section for Cognitive Systems, which is Denmark's leading group for machine learning research. The group aims for the highest quality research was e.g.