Wonder why your Ph.D./Master's application is being rejected?

Here are some insider reasons (from a committee member) that result in such "Love Letters" 👇

- A Short Thread

1) First, you should know that sometimes, an independent reviewer is required (especially in the US) to review applications even after PIs must have selected their candidates. So, even if any bias slips through the PI, the independent reviewer will pick it up.
2) Having said this, there are three major issues that application reviewers/committees often find with your application. These three components make your applications almost challenging to assess.
3) NUMBER ONE - Incomplete Applications

This occurs when you do not upload the requested documents needed to evaluate your file. Think of a candidate that did not send a GRE score when the institution clearly requires it for admission.
4) NUMBER TWO - Error laden Essays

This is particularly interesting because based on reports from this resource person, it is not particularly about how little your experience is but how you communicate it error-free or at least extensively minimized. This gives headache!
5) "Too many errors and running sentences pisses me off and I begin to wonder if this applicant really took this application seriously or just submitted his or her application as part of many mass submissions just to make up the numbers", says this resources person. Take note!!
6) NUMBER THREE - Generic recommendation letters

This often presents itself as bland and generic. This kind of letter does not attempt to share practical engagements or activities carried out by the student in collaboration or independently of an advisor.
7) Filled with many "She is intelligent, intuitive, and hard-working". Okay? so can you put this in perspective? Can you give examples of situations where the student exhibited these qualities as claimed?

Now you know! Fix this ASAP.

#BigDaddyTweets #phdchat

More from Science

Hard agree. And if this is useful, let me share something that often gets omitted (not by @kakape).

Variants always emerge, & are not good or bad, but expected. The challenge is figuring out which variants are bad, and that can't be done with sequence alone.


You can't just look at a sequence and say, "Aha! A mutation in spike. This must be more transmissible or can evade antibody neutralization." Sure, we can use computational models to try and predict the functional consequence of a given mutation, but models are often wrong.

The virus acquires mutations randomly every time it replicates. Many mutations don't change the virus at all. Others may change it in a way that have no consequences for human transmission or disease. But you can't tell just looking at sequence alone.

In order to determine the functional impact of a mutation, you need to actually do experiments. You can look at some effects in cell culture, but to address questions relating to transmission or disease, you have to use animal models.

The reason people were concerned initially about B.1.1.7 is because of epidemiological evidence showing that it rapidly became dominant in one area. More rapidly that could be explained unless it had some kind of advantage that allowed it to outcompete other circulating variants.

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