Dr. John Wiedenhöft

Research Associate, Scientific Core Facility Medical Biometry and Statistical Bioinformatics

telephone: +49 (0 ) 551 39-63371

fax: +49 (0) 551 39-4995

e-mail address: john.wiedenhoeft(at)med.uni-goettingen.de

location: Humboldtallee 32, EG 153

ORCID iDorcid.org/0000-0002-6935-1517

Short biography

[Since 2019]

Research Associate at the Scientific Core Facility "Medical Biometry and Statistical Bioinformatics"

[2017 - 2018]

Postdoctoral Researcher in the Department of Computer Science and Engineering at Chalmers University of Technology (Gothenburg)

[2011 - 2018]

Ph.D. in Computer Science in the Department of Computer Science at Rutgers University

[2010 - 2011]

Research assistant at Max Planck Institute for Molecular Genetics (Berlin) at the Department of Computational Molecular Biology, working on phylogeny of multidomain proteins, biclustering of gene expression data


Visiting scholar at Iowa State University


M.Sc. in Bioinformatics (Free University of Berlin and Charité)


Research assistant in zebra finch RNA-seq project at Max Planck Institute for Molecular Genetics in Berlin


Research assistant at Free University of Berlin, Department of Animal Behaviour in the project: "Do birds tango? Biological origins of rhythm as a carrier of emotions" (Cluster of Excellence "Languages of Emotion")


Research assistant at the Max Planck Institute for Evolutionary Anthropology: Algorithms for human genomic diversity in population genetics and phylogenetic approaches towards the evolution of Bantu languages

[2007 - 2009]

B.Sc. in Bioinformatics (Free University of Berlin and Charité)


Ethnomusicological field research with the Newar in Bhaktapur in the Kathmandu Valley (Nepal): The role of the dhimay in ritual and processional music during the biskah jatra


Magister studies, in musicology (Humboldt-University Berlin), ethnomusicology and Indian philology (Free University of Berlin) as well as communication research (Technical University Berlin)

Theses section

  1. PhD Thesis: ‘Dynamically compressed Bayesian Hidden Markov Models using Haar wavelets’
  2. Master’s Thesis: ’Biclustering and Related Methods’
  3. Bachelor’s Thesis: ’ Phylogenetic Reconstruction of Ancestral Multidomain Proteins’


Follow us