Future projects and goals

Large-scale systematic maps of gene-gene and gene-environment interactions by automated phenotyping, using image analysis, machine learning, sparse model building and causal inference.
DNA-, RNA- and ChIP-Seq and their applications to gene expression regulation: statistical and computational foundations.
Cancer genomics, genomes as biomarkers, cancer phylogeny.
Image analysis for systems biology: measuring the dynamics of cell cycle and of cell migration of individual cells under normal conditions and many different perturbations (RNAi, drugs).

More @ http://www.embl.de/research/units/genome_biology/huber/index.html

Asst. PROF IN BIOINFORMATICS.

QUALIFICATION: AS PER UGC

DESIRABLE: 1 YEAR EXPERIENCE IN ACADEMICS

CONTACT immediately

Prof D.S.Bhatia
Director
9351288070

Last date within 7 days of the publication.

Find more @ http://jnujaipur.ac.in/downloads/AdvtDec2012.jpg

Research Interest and Activities: 1. Plant Genetic Transformations
2. Molecular Population Genetics 3. Detection of human and Animals diseases 4. Breast Cancer­specific protein marker 5. Phytochemical 6. Genomic map 7. Bioremediation 8. Tissue Culture.

Web site and links: www.geocity.cbge.com

http://www.uib.no/rs/mcb

---how genetic variation is distributed within and between individuals,
---determining how this diversity changes over evolutionary time.

Hence, Cox group work at the interface between biology, statistics and computer science to address questions of outstanding biological importance through intrepretation of large genetic datasets.

Profile:
Associate Professor Murray Cox,
Inaugural Rutherford Fellow of the Royal Society of New Zealand, Principal Investigator in the BioProtection Research Center and Associate Investigator in the Allan Wilson Center for Molecular Ecology and Evolution
Email : m.p.cox@massey.ac.nz
Webpage: http://massey.genomicus.com/index.html

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/

Available immediately until 30th November 2015, to work on the development of bioinformatics approaches to aid analysis of data derived from the metabolomic profiling of biological matrices. The successful applicant will lead research activities on an FP7 funded EU-wide collaborative project aimed at establishing biomarker-based strategies for high throughput diagnostic screening. Key tasks will involve multivariate analysis of large datasets, bioinformatic-based selection and validation of identified markers, construction of metabolomic spectral profile databases and development of machine learning/database searching approaches amenable to analytical screening techniques. This position will offer the opportunity to travel and undertake work with project collaborators based in the Republic of Ireland and Europe.

Informal enquiries may be directed to Dr Terry McGrath, email: terry.mcgrath@qub.ac.uk.

Anticipated interview date: Thursday 31st October 2013
Salary scale: £30,424 – £39,649 per annum (including contribution points)
Closing date: Monday 21st October 2013

Telephone (028) 90973044 FAX: (028) 90971040 or e-mail on personnel@qub.ac.uk

The University is committed to equality of opportunity and to selection on merit. It therefore welcomes applications from all sections of society and particularly welcomes applications from people with a disability.

Fixed term contract posts are available for the stated period in the first instance but in particular circumstances may be renewed or made permanent subject to availability of funding.

More @ https://hrwebapp.qub.ac.uk/tlive_webrecruitment/wrd/run/ETREC107GF.open?VACANCY_ID=5616943npO&WVID=6273090Lgx&LANG=USA

1. Understand the Landscape

Before diving into applications, take the time to understand the bioinformatics job market. Common roles include:

• Bioinformatics Analyst/Scientist: Focused on data analysis and interpretation.
• Computational Biologist: Combines computational techniques with biological research.
• Data Scientist in Genomics: Applies machine learning and statistical models to genomic data.
• Software Developer in Bioinformatics: Designs and develops tools and pipelines for biological research.

Familiarize yourself with the key industries hiring bioinformaticians, such as academia, biotech, pharmaceuticals, healthcare, and agriculture.

2. Build a Strong Foundation

Bioinformatics demands a diverse skill set. Ensure you have a solid foundation in the following areas:

• Programming Skills: Proficiency in Python, R, or Perl is often required. Familiarity with tools like Bash scripting and version control systems (e.g., Git) is a plus.
• Statistics and Data Analysis: Knowledge of statistical methods, machine learning, and data visualization is crucial.
• Biological Knowledge: Understanding genomics, transcriptomics, and proteomics will help you communicate effectively with biologists.
• Specialized Tools and Databases: Be comfortable using tools like BLAST, Bowtie, and databases like NCBI and Ensembl.

3. Create a Winning Resume and Portfolio

Highlight your technical skills, biological knowledge, and relevant experience. Tips for a standout application:

• Tailor your resume to each job, emphasizing skills mentioned in the job description.
• Showcase your experience with real-world datasets by linking to your GitHub profile or online portfolio.
• Include details of any publications, presentations, or significant projects.

4. Network Actively

Networking is often the key to discovering opportunities. Here’s how to build connections:

• Attend Conferences and Workshops: Events like ISMB or specialized bioinformatics workshops are great for meeting professionals.
• Engage Online: Join LinkedIn groups, participate in bioinformatics forums, and follow relevant hashtags on Twitter.
• Leverage Alumni Networks: Connect with alumni from your university who are working in the field.

5. Gain Relevant Experience

Experience is a major factor for hiring managers. Ways to enhance your profile include:

• Internships: Seek out internships in research labs or biotech companies.
• Collaborations: Volunteer to work on projects with professors or peers.
• Open Source Contributions: Participate in bioinformatics software development on platforms like GitHub.

6. Prepare for Interviews

Bioinformatics interviews often combine technical and behavioral questions. Prepare by:

• Reviewing Key Concepts: Refresh your knowledge of algorithms, sequence analysis, and statistical methods.
• Practicing Coding: Be ready to solve coding challenges or discuss code snippets.
• Understanding the Organization: Research their recent projects, publications, or products.
• Preparing Questions: Demonstrate interest by asking about their tools, workflows, or team structure.

7. Stay Resilient and Persistent

Job hunting can be a long process, but persistence pays off. Tips to keep moving forward:

• Keep improving your skills by taking online courses or certifications.
• Stay updated with advancements in bioinformatics by following journals and blogs.
• Apply to multiple positions and don’t get discouraged by rejections. Each application is a learning experience.

Closing Thoughts

Landing a bioinformatics job requires a mix of technical expertise, networking, and resilience. By understanding the market, showcasing your skills effectively, and continuously learning, you’ll be well on your way to a rewarding career in this dynamic field. Remember, the key to cracking the code is perseverance—stay curious, stay determined, and success will follow.

Deadline for applications : January 15, 2014.

Description :

We seek a talented and motivated post-doc to develop computational methods for inferring the molecular basis of genetic diseases by integration of personal omics data. Research topics include: identifying causal mutations of rare disease patients by meta-analysis; inferring disease-causing molecular pathways from genotype, human phenotypes, and omics profile of patient-derived cell lines; and causal inference from longitudinal omics studies of patients. The developed methods will be applied to analyze data from our medical collaborators.

Candidates must either hold a PhD in computational biology or bioinformatics, or hold a PhD in physics, statistics, or applied mathematics with practical experience with high-dimensional data analysis. Experience in quantitative genetics is a plus. Applicants must have a proven publication record and an interest for translational research.

The Gagneur lab is a young, lively and multidisciplinary group with a research focus on systems genetics and gene regulation. It is located at the Gene Center of the LMU (University of Munich), an interdisciplinary institution whose 16 independent research groups investigate the regulation of gene expression at all levels - from the underlying molecular mechanisms to the biological system. The institute is located on the biomedical research campus Munich-Grosshadern, offering a dynamic, interactive and internationally oriented research environment. The dynamism of Munich and the proximity of the Alps provide an excellent quality of life.

The salary is according to the TV-L (German academic salary scale).
Applications including a cover letter, CV, and references must be sent by January 15th 2014 to Julien Gagneur (gagneur@genzentrum.lmu.de)

About the lab: http://www.gagneur.genzentrum.lmu.de