AcademicLabs company logo
AcademicLabs
Actively Hiring
Uniting the world's scientific experts, companies and knowledge in 1 place1-10 Employees
  • Responds within three weeks
    Based on past data, AcademicLabs usually responds to incoming applications within three weeks
  • Early Stage
    Startup in initial stages
Website
Locations
Company size
1-10 people
Total raised
$2.3M
Company type
Startup
Markets
SaaSEnterprise SoftwareAnalyticsBusiness IntelligenceBig DataCollaborationProfessional NetworkingSocial NetworkScienceB2B · SaaS · Mobile · Artificial Intelligence / Machine Learning

Jobs at AcademicLabs

At AcademicLabs, we have a skyrocketing ambition of advancing human progress by accelerating research and innovation on a global scale. To do this, we unlock the enormous untapped R&D potential hidden behind the walls of academia, and introduce transparency to the fragmented world of research. We aim to become the #1 global R&D matchmaking platform, combining a professional social network custom-built for research, best-in-class search engine for research partner discovery, and an integrated collaboration solution. As a team, we combine years of experience in software engineering, academic research, and the scientific publishing industry. We won multiple awards, were selected in 3 great startup incubators/accelerators, and are backed by captains of industry and entrepreneurs, a.o. (former) VPs and CTOs of Oracle, Total, Coca-Cola, Broadsoft, Bekaert and Teamleader.
Filter by
Team
Location
Type
Sales

Pharma/Biotech Sales Agent in a Startup ("google for science"), Commission-only

NewPosted 1 month ago

Pharma/Biotech Sales Agent in a Startup ("google for science"), Commission-only

  • science,
  • the translation of science to innovation on the market,
Engineering

Software engineer, data engineering (for scientific intelligence platform)

NewPosted 2 months ago

Software engineer, data engineering (for scientific intelligence platform)

  • Facilitating the process of adding and updating data from a wide variety of sources — Sources can vary wildly in terms of: data quality, data quantity, data relevance, how structured the data is, how the data is gathered, etc.
  • Streamlining the workflows to extract structured information from raw, unstructured data — This...