From the Northeastern Section of the ACS, focusing on career management and development

November 2021
« Sep    
Preparing for Career Paths in Graduate School.
Filed under: Position Searching, Job Offer (Situations), Mentoring, Alternate Career Paths
Posted by: site admin @ 9:59 am

The seminar on the titled topic highlighted three
key areas that people in grad school can focus on
in addition to items successful predecessors pointed
, namely:

  - get out of the lab and meet people from different 
backgrounds, for the degree alone is not enough
  - develop critical thinking skills that will help
writing communication, audience analysis in dealing
with different audiences and 
  -  be on positive terms with your advisor and committee
members so that they want to be allies for you in your
career for the long term.

The first TOPIC area was GRIT.  That is perseverance in the face
of challenges.  Angela Duckworth has pointed out its value
and ways we can gain this formidable character element.
We see this essential in all career paths.

The second area was TRUST.  
Trust is an unmistakable key allowing teams be effective and
successful.  We had a true life story about how a manager
in a company micro-managed a professional repeatedly
returning to find out results.  It was done to the point of
indicating a lack of trust and commitment.  
Trust between all levels in an organization or in a partnership
is something we can learn and be able to foster and recognize.

The third was bringing out the concept and examples of
Bayesian thinking to develop as a critical thinking tool.
We had a working example and then lively discussion
how this is applied in a job search where a person accepted
a temporary position.  Then he navigated unemployment to
receive four interviews and multiple offers helped by
the short term position experience.

3 Responses to “Preparing for Career Paths in Graduate School.”

  1. site admin Says:

    Bayesian statistics and probabilistic thinking.
  2. site admin Says:
    Practical discussion of probability estimates in scientific discussion is presented in.

    “Bayesian networks during their deliberations. Given the
    growing trend in applying Bayes’ theorem and likelihood
    ratios to forensic interpretations (10), it may be helpful to
    consider how Bayes’ theorem can be applied to spectroscopic
    or drug chemistry casework.

    For example, what are the (posterior) odds that a baggie of
    white powder seized from a suspect actually contains cocaine
    versus another innocuous substance, given that the powder
    was found to contain cocaine by a seized drug analyst?

    Some useful background information in the case might be
    as follows:
    First, the baggie was seized during the process of a tape-recorded
    drug deal for cocaine, and second, the powder gave
    unambiguously positive results for cocaine using two color spot
    tests, FT-IR, and GC–MS, which meets the standards set forth
    in E2329-14. Determining the posterior odds requires taking
    the product of the prior odds and the likelihood ratio (11).

    The prior odds includes values such as the probability that a
    suspect caught in a drug deal for cocaine would happen to
    have a baggie of white powder containing cocaine. It’s
    hard to conceive of a probable reason why a non-drug-dealing
    person would carry a baggie of white powder, or why a
    suspected drug dealer would broker a deal without having
    the contraband in his possession, which is to say that even
    in the absence of any chemical tests, the prior odds alone
    would indicate that the white baggie has a very high
    probability of containing cocaine.

     The likelihood ratio of  the testing result is at the heart of this
    discussion, and its value is determined by the analytical
    scheme and the frequency of incorrect determinations. The
    likelihood ratio takes into account the frequency of true
    negatives, true positives, false negatives, and false
    positives for the analytical scheme.”

     10) F. Taroni, A. Biedermann, S. Bozza, P. Garbolino, and
    C. Aitken, Bayesian Networks for Probabilistic Inference and
    Decision Analysis in Forensic Science, 2nd Ed. (Wiley, New
    York, New York, 2014).
     11) T. Bayes and R. Price, Phil. Trans. Royal Soc. London
    53, 370–418 (1763).
  3. site admin Says:


Leave a Reply