Postdoc position (m/w/d)

in the field of computational evolutionary microbiology is available
for an initially limited period of 36 months at the earliest possible
date. The weekly working time corresponds to 100% of full employment
(If the legal requirements under collective bargaining law are met, the
tariff grouping is carried out up to pay scale 13 TV-L. The obligation
to teach amounts to 4 hours.

The Genomic Microbiology Group research interests are focused on
microbial genome evolution with an emphasis on the study of lateral gene
transfer. In our research we use both computational and experimental
approaches (see www.uni-kiel.de/genomik). The position offers the
opportunity to develop an independent research profile within the group
research focus. The successful applicant is expected to be involved
in teaching of bioinformatics and molecular evolution, including the
development of teaching materials (lectures/exercises/short videos).

Your profile:
· Doctoral or PhD degree in Molecular Evolution, Bioinformatics or
related fields.
· Knowledge and experience in programming (e.g., Python) and
biostatistical analysis (e.g., with R or MatLab).
· Any of the following expertise is an advantage: the analysis of
genomic or transcriptomic data, phylogenetic reconstruction,
comparative genomics.
· Good oral and written communication skills in English.
· Ability to teach in German is an advantage (alternatively, an
indication to do so from the 2nd year on).
· Skills and motivation to communicate and interact with other
scientists.

The Christian-Albrechts-University sees itself as a modern and
cosmopolitan employer. We welcome your application regardless of your age,
gender, cultural and social background, religion, ideology, disability
or sexual identity. We promote equality of the sexes.

The Christian-Albrechts-University is committed to the employment of
people with disabilities. Preference will be given to applications from
severely handicapped persons and persons of equal standing, provided
they are suitable.

We expressly welcome applications from people with a migration background.

For enquiries regarding the position, teaching obligations and research
topic please contact Prof. Tal Dagan: tdagan@ifam.uni-kiel.de.

Applications should be submitted by email to Mrs. Haacks
(dhaacks@ifam.uni-kiel.de) as a single PDF and include: (1) a letter of
motivation (max 1 page, Arial 11, line spacing 1.15), (2) CV, (3) PhD
certificate. Please use 'GMG postdoc application - [your name]'
as a subject.

Please, refrain from sending us application photos.

Application deadline: August 31 2021 or until the position is
filled. Interviews will take place during September/October 2021. The
planned starting date for the position is flexible (but in 2021).

As these positions are funded by the South African National Research Foundation (NRF) priority will be given to South African citizens and permanent residents however exceptional applicants who do not fulfil these criteria may be considered.

Applications including a CV outlining your experience together with a cover letter detailing why you are a suitable candidate should be sent to simon_at_sanbi.ac.za by 30th September 2013.

More @ http://www.sanbi.ac.za/hiv-phd-and-msc-research-positions/

1. NCBI (National Center for Biotechnology Information):

   • NCBI provides various databases related to genetic information, including 16S rRNA sequences.
   • You can access the 16S ribosomal RNA sequences from NCBI's Nucleotide database (https://www.ncbi.nlm.nih.gov/nucleotide/).
   • Perform a search using keywords like "16S rRNA" or specific bacterial names to find relevant sequences.
   • You can download sequences individually or in batches using the provided tools.

2. GreenGenes:

   • GreenGenes is a widely used 16S rRNA gene sequence database.
   • You can access it at http://greengenes.secondgenome.com/.
   • GreenGenes provides precompiled databases for various purposes, including classification, alignment, and phylogenetic analysis.

3. SILVA:

   • SILVA (https://www.arb-silva.de/) is another comprehensive database for ribosomal RNA (rRNA) sequences.
   • It covers not only 16S rRNA but also other ribosomal RNA sequences.
   • SILVA provides precompiled databases for various purposes, including taxonomic classification and alignment.

4. Ribosomal Database Project (RDP):

   • RDP (http://rdp.cme.msu.edu/) is a curated database that offers 16S rRNA sequences.
   • It provides tools for sequence analysis and classification.
   • You can download sequences and taxonomy information from their website.

5. QIIME (Quantitative Insights Into Microbial Ecology):

   • QIIME (https://qiime2.org/) is a widely used bioinformatics platform for microbiome analysis.
   • It provides tools for analyzing microbial communities, including processing 16S rRNA sequences.
   • QIIME often includes its own preprocessed 16S rRNA databases that can be used for analysis within the platform.

Before downloading any database, make sure to read the terms of use and citation requirements, as some databases may have specific usage policies. Additionally, consider the compatibility of the database with your analysis pipeline and software tools.

NCBI 16s RNA database location ftp://ftp.ncbi.nih.gov/blast/db/16SMicrobial.tar.gz

Models and software for predicting who is at risk of carrying genetic variants that confer susceptibility to cancer. Application to breast, ovarian, colorectal, pancreatic and skin cancer.

Statistical methods for the analysis of high throughput genomic data: analysis of cancer genome sequencing projects; integration of genomic information across technologies; cross-study validation of genomics results.

Statistical methods for comparative effectiveness research: comprehensive models for lifetime history of chronic disease outcomes; Bayesian meta-analysis; Bayesian causal inference; decision analysis.

Bayesian modeling and computation: multilevel models; decision theoretic approaches to inference; sequential experimental design and their application to adaptive and multistage studies in clinical and epidemiological research.

http://bcb.dfci.harvard.edu/~gp/index.html

http://scholar.google.com/citations?user=OlpYP3UAAAAJ&hl=en

Understanding DNA-protein interactions, genome engineering
Computational Genomics and Bioinformatics
Secondary metabolite biosynthesis, metabolic engineering

Ongoing Projects:

"Betraying the parasite’s redox system: Studies on spermidine synthase of Leishmania donovani "

"Towards modifying nature's DNA-recognition system"

"Yield enhancement strategies for production of therapeutic compounds by cell and tissue cultures of Tinospora cordifolia (willd.) Miers ex Hook. F. & Thoms"

"Program support for Computational Genomics"

More at http://web.iitd.ac.in/~sundar/

The group is both computational and experimental.

We ask biological questions to large datasets made using novel genomics techniques, with the help of computers. One of the strengths in the group are the many connections to high-profile experimental laboratories which supply data to be analyzed.

Lab webpage @ http://people.binf.ku.dk/albin/Sandelin_group_at_the_Bioinformatic_Centre/The_Sandelin_group.html

Contact The Director, National Bureau of Agriculturally Important Microorganisms, Kusmaur, Maunath Bhanjan-275101 (U.P.); Phone: 0547-2530080, Fax: 0547-2530358, e mail: nbaimicar@gmail.com; website: www.nbaim.org.in OR

Dr. Dhananjaya P. Singh, Senior Scientist & CCPI, NABG project, NBAIM, Maunath Bhanjan, 275101; Mob.- 09415291703; e mail - dpsfarm@rediffmail.com, nabg.nbaim@gmail.com

More at http://www.nbaim.org.in/Announc.aspx?cd=36

The project is on bacterial gene gain and loss and emerging pathogenicity, and is described in full here: https://www.ebi.ac.uk/research/postdocs/ebpods/projects/goldman-welch-2015 . The EMBL-EBI / Cambridge Computational Biomedical Postdoctoral (“EBPOD”)

The closing date for applications is 3 September 2015. Nick Goldman EMBL-European Bioinformatics Institute Nick Goldman

More at https://www.ebi.ac.uk/research/postdocs/ebpods/projects/goldman-welch-2015

Gene expression and protein expression patterns between normal and non-normal tissues is a growing area of research that may lead to the identification of candidate genes for understanding the etiology of common, complex diseases.

Lab homepage @ http://ritchielab.psu.edu/ritchielab/