PhD defence by Richard Michael

Model, Acquire, and Measure under Uncertainties - Principled Bayesian Optimization and Active Learning for Data-efficient Protein Engineering

Assessment Committee

(Chairperson) Associate Professor Alexander Hauser
Associate Professor Mikkel N. Schmidt
Lecturer (corresponding to assistant professor) Henry Moss

Supervisors

Professor Wouter Krogh Boomsma

Place

The defence is conducted as a hybrid defence.

To attend the digital defence, please follow the link:

https://ucph-ku.zoom.us/j/65783342097?pwd=gOh9sturFZfQ0DRx7T3ZP3kYwPHCoM.1

Instructions if you wish to attend the defence via the digital solution: Please follow the link and hereafter the instructions to download the required -client. If the -client is incompatible with your pc, smartphone etc. you can attend via an Internet browser. Log-in in due time before to allow time to install the -client.

The physical place of the defence:

Niels Bohr Building, Ground floor (0), Room: NBB 001-0-EF.000 - Margrethe Bohr Salen, Jagtvej 128-132, 2200 København N

Ask for a copy of the thesis here: Richard.michael@di.ku.dk or r.michael@posteo.net