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Summer Intern - Computational Biology

Takeda, Tokyo, Japan
Employer Description

Takeda is a patient-focused, values-based, R&D-driven global biopharmaceutical company committed to bringing Better Health and a Brighter Future to people worldwide. Our passion and pursuit of potentially life-changing treatments for patients are deeply rooted in over 230 years of distinguished history in Japan.

Job Description

POSITION SUMMARY
 

A Ph.D. student level summer intern position in computational biology (Global Computational Biology) within Takeda Boston that is tasked with supporting the efforts for target identification.

 

Job Requirements

DUTIES & RESPONSIBILITIES

  • Develop method utilizing deep learning or other machine learning framework to predict function of microbial proteome.
  • Run literature search and method evaluation.
  • Write reports and give presentations on the analysis results to internal/external audience.

 

QUALIFICATIONS

  • A PhD student in computational biology, bioinformatics, human genetics or statistical genetics, biostatistics, computer science, mathematics or relevant engineering fields.  
  • Strong quantitative science background including statistics, data mining, and machine learning, particularly deep learning algorithms.
  • Strong knowledge on human genetics or statistical genetics.
  • Good experiences with statistical modeling for associating genetic features with phenotypes such as treatment response or disease subtypes.
  • Experiences with next-generation sequencing data analysis are appreciated.
  • Sufficient programming exposure to R. Experiences with Python, Perl, Matlab, C/C++, or JAVA are also appreciated.
  • Experience with tensorflow preferred.
  • Excellent communication skills.

 

Locations

Boston, MA

Worker Type

Employee

Worker Sub-Type

Paid Intern (Fixed Term) (Trainee)

Time Type

Full time

JOB DETAILS
POSTED
12 February 2019
CLOSES
13 May 2019
JOB TYPE
Internship
PREFERRED EDUCATION
Doctorate/PhD
SALARY (USD)
Open
TITLE / ROLE
Intern
INDUSTRY / FIELD
Biology